untitled
A Frequent Keyword-set based Algorithm for Topic
Modeling and Clustering of Research Papers
Kumar Shubhankar
Centre for Data Engineering
IIIT Hyderabad
Hyderabad, India
shubankar@students.iiit.ac.in
Aditya Pratap Singh
Centre for Data Engineering
IIIT Hyderabad
Hyderabad, India
aditya_pratap@students.iiit.ac.in
Vikram Pudi
Centre for Data Engineering
IIIT Hyderabad
Hyderabad, India
vikram@ iiit.ac.in
Abstract – In this paper we introduce a novel and efficient
approach to detect topics in a large corpus of research
papers. With rapidly growing size of academic literature, the
problem of topic detection has become a very challenging
task. We present a unique approach that uses closed
frequent keyword-set to form topics. Our approach also
provides a natural method to cluster the research papers
into hierarchical, overlapping clusters using topic as
similarity measure. To rank the research papers in the topic
cluster, we devise a modified PageRank algorithm that
assigns an authoritative score to each research paper by
considering the sub-graph in which the research paper
appears. We test our algorithms on the DBLP dataset and
experimentally show that our algorithms are fast, effective
and scalable.
Keywords. Closed Frequent Keyword-set, Topic Detection,
Graph Mining, Citation Network, Authoritative Score
I. INTRODUCTION
Thousands of research papers are published every
year spanning various fields of research. These research
papers cover broad topics like Data Mining, Artificial
Intelligence, Information Extraction, etc., which can be
further divided into several sub-topics, sub-sub-topics and
so on. Classification of research papers into the broad
topics is a trivial problem which is generally dealt
manually. But more often than not, a researcher is more
interested in literature related to specific topics within
these broader topics. For example, a researcher might be
more interested in knowing about topics like Data
Visualization, Frequent Pattern Mining, etc., which are
different sub-topics within the broader field of Data
Mining. Topic discovery has recently attracted
considerable research interest [13, 15, 10, 21, 22, 24, 19,
4, 18, 8, 17]. Also, the researchers are often interested in
knowing the important research papers from their topics
of interest.
In this paper, we propose a novel and efficient
method for topic modeling and clustering of research
papers. The keywords associated with a paper can be used
to deduce the topics the paper deals with. Often these
keywords are fed as meta-data by the authors in the paper
itself. But this is not the case for all the research papers.
We need to have a deterministic way of determining the
topics of concern for all the research papers. Based on the
intuition that a document is well summarized by its title
and the title gives a fairly good high-level description of
its content, we use the keywords present in the title of a
paper to detect the topics. It is to be noted that we do not
use abstract of the paper for extracting phrases as there
are lots of irrelevant phrases in the abstract present as
noise.
In the proposed approach, we form closed frequent
keyword-sets by top-down dissociation of keywords from
the phrases present in the titles of papers on a user-
defined minimum support. All the research papers sharing
a topic form a natural cluster. We also propose a time
independent, modified iterative PageRank algorithm to
rank the research papers in a topic cluster which assign an
authoritative score to each paper based on the citation
network. For a topic T we consider all the research papers
containing that topic and the citation edges of these
papers. We can determine the important research paper
belonging to a given topic T by looking at the scores of all
the research papers belonging to that topic. It is to be
noted that a research paper could belong to a number of
clusters forming hierarchical, overlapping clusters.
These algorithms have many applications like
recommendation systems, finding landmark papers, trend
analysis etc. We test our algorithms on the DBLP dataset.
Our experiments produce a ranked set of research papers
corresponding to each topic that on examination by field
experts and based on our study match the prominent
papers from the topics in the dataset.
II. RELATED WORK
Topic extraction from documents has been studied
by a lot of researchers. Most work on topic modeling is
statistics-based like the work by Christain Wartena and
Rogier Brusse [23], which uses most frequent nouns, verbs
and proper names as keywords and clusters them based on
different similarity measures using the induced k-bisecting
clustering algorithm. There has also been some NLP-based
work on topic detection like the work by Elena Lloret [16].
Our work is based on dissociation of phrases into frequent
keyword-sets, which as shown in section 7 is very fast and
highly scalable.
Clustering documents based on frequent itemsets
[2] has been studied in the algorithms FTC and HFTC [3]
and the Apriori-based algorithm [14]. Both of these works
consider the documents as bags of words and then find
frequent itemsets. Thus, the semantic information present
in the document is lost. We extract phrases from the titles
of the research papers and derive frequent substrings as
frequent keyword-sets, maintaining
semantics. Another work that talks abou
of semantics while forming topics is by
[6], where the authors consider a wind
they find the itemsets which are candidat
within this window, the relative positio
considered insignificant. We, on the
considered only sequential patterns with
quite intuitive that if we do not con
positions of the keywords, there might b
semantics are lost.
We have used closed [20] freque
similarity measure rather than maximal
set as used by Ling Zhuang et al. [25]
document clustering. We cannot use
keyword-sets as topics because the
information is lost as it considers only th
keyword-set.
Topic summarization and analy
documents has been studied by Xueyu
Wang [9]. They have used LDA mode
and Kullback-Leibler divergence as simi
clustering the documents. The drawback
that it needs a pre-specified number of
manual topic labeling. In our study, no p
topics is required. The work in [11] us
between the distribution of terms repres
the distribution of links in the citation
documents containing these terms. We o
use frequent keyword-sets to form the top
citation links to detect important topics
derived.
Rest of the paper is organized as
describes our topic modeling algor
discusses our approach to cluster the re
topics. Section 5 describes our algorith
research papers using citation network.
the experiments and results. Section 7
applications of our algorithms and sectio
work.
III. OUR APPROAC
The method proposed by us i
formation of keyword-sets from titles
papers and finding closed frequent keyw
the topics.
Definition 1. Phrase: A phrase P is def
words between two stop-words in the ti
paper.
Definition 2. Keyword-set: We define a
an n-gram substring of a phrase, wher
number.
Definition 3. Frequent Keyword-set: A
said to be frequent if its count in the c
than or equal to a user-defined minimum
the underlying
ut the preservation
Zhou Chong et al.
dow within which
tes for topics. But,
on of the words is
other hand, have
hin a phrase. It is
nsider the relative
be cases where the
ent keyword-set as
frequent keyword-
] in their work on
maximal frequent
en most of the
he longest possible
ysis on academic
Geng and Jinlong
l to extract topics
ilarity measure for
k of LDA model is
f latent topics and
prior knowledge of
ses the correlation
senting a topic and
graph among the
on the other hand,
pics and utilize the
among the topics
follows: Section 3
rithm. Section 4
search papers into
hm for ranking of
Section 6 presents
talks about some
on 8 concludes our
H
is based on the
of the research
word-sets to form
fined as a run of
itle of a research
keyword-set K as
re n is a positive
keyword-set K is
corpus is greater
m support [1].
Definition 4. Closed Frequent Key
closed frequent keyword-set as a
none of whose supersets has the sa
papers as it. Each closed frequent k
a unique topic T.
A. Phrase Extraction
Given the title of a research p
its phrases Pij. As defined above,
words between two stop-words from
of 671 Standard English stop-word
is thus mapped to the corresponding
title.
Our original problem domain
R1 – [P11, P12, P13
R2 – [P21, P22, P23
…
Here, Ri represents the i
th r
represents its jth phrase. Our ne
keyword-sets from these phrases, di
into keyword-sets. It should be not
problem domain, we will have t
phrase multiple times in a single s
occur in several research papers. S
problem domain, mapping each p
research papers Rij it belongs to
dataset. In this domain, each phra
only once. The problem domain
follows:
P1 – [R11, R12, R13
P2 – [R21, R22, R23
…
B. Keyword-set Extraction
As defined above, a keywor
substring of a phrase P. In our method,
the substrings of the phrases as keyw
relative ordering is maintained, pre
semantic of the phrases. Each keywo
semantic unit that can function as a
knowledge discovery and hence is a pot
As an example of keyword-set
phrase ABCDE, the potential frequent k
all the ordered substrings. Thus, ABC
keyword-sets, as shown in figure 1.
Figure 1. Set of all keyword-sets obtaine
Note that ABCDE is a phr
keywords A, B, C, D and E. Find
yword-set: We define a
frequent keyword-set
ame cluster of research
keyword-set represents
paper Ri, we extract all
a phrase is a run of
m a comprehensive list
ds. Each research paper
g phrases present in its
n is:
3, …]
3, …]
research paper and Pij
ext step is to derive
issociating each phrase
ted that in the original
to dissociate a given
scan as a phrase might
o, we reverse map the
hrase Pi to the set of
, in one scan of the
ase will be dissociated
is thus modified as
3, …]
3, …]
rd-set K is basically a
we have considered only
word-sets and hence the
eserving the underlying
ord-set thus formed is a
basic building block of
tential topic.
extraction, consider the
keyword-sets are the set of
CDE gives the following
ed from phrase ABCDE
rase consisting of the
ding all the substrings
requires a simple implementation of queue in top-down
fashion, taking O(1) time at each level and O(n) time
overall. We consider only the substrings of a phrase rather
than the power set of the keywords in the phrase. Thus,
finding the keyword-sets requires O(n) time instead of
O(2n).
C. Frequent Keyword-set Formation
Frequent keyword-sets are formed on a user-
defined minimum support. Frequent keyword-sets are
those keyword-sets whose support is no less than the
minimum support. The supports of the keyword-sets are
calculated during the generation of the keyword-sets from
the phrases in the second scan.
It is to be noted that the length of the list of
research papers corresponding to a phrase is its support,
assuming that a phrase occurs not more than once in the
title of the research paper. In the first scan, we cannot
eliminate the phrases whose support is less than the
minimum support as two or more phrases can share the
same keyword-set whose combined support might be
greater than the minimum support. The elimination of
non-frequent keyword-sets would be done only after all
the keyword-sets, along with their supports, have been
generated in the second scan of the dataset.
The algorithm to increment the support and add
research papers to a given keyword-set is shown below:
Procedure 1: Frequent Keyword-set Generation
Require: phraseKeys PK, minimum support min_sup
1: for each phrase P in PK
2: keywordSetList KSL= findAllSubstringOf(P)
3: for each keywordSet K in KSL
4: keywordSetCount[K] += 1;
5: add paper R to keywordSetPaperList[K]
6: for each keywordSet K in keywordSetCount
7: if keywordSetCount[K] < min_sup
8: delete(keywordSetCount[K])
9: delete(keywordPaperList[K])
In the procedure 1, all the frequent keyword-sets
are derived along with their supports. From step 1 to step
5, all the keyword-sets of each phrase are derived and
their supports in keywordSetCount and the corresponding
paper list in keywordSetPaperList are updated. From step
7 to step 10, we delete those keyword-sets whose supports
are less than the minimum support. The keyword-sets thus
derived are the frequent keyword-sets.
We have derived the frequent keyword-sets in a
top-down fashion. As said before, we only need the
substrings of the phrases and not the subsets, which would
take O(2n) time. Traditional association rule mining
algorithms like Apriori that require one scan of the dataset
to calculate the supports of the itemsets at each level take
too much time and space. In our algorithm, we require
only 2 scans of the dataset to calculate the supports of all
the candidate keyword-sets. Since our algorithm runs in
linear time compared to exponential Apriori-like
algorithms and takes only 2 scans of the dataset to
calculate the supports, our algorithms are fast and highly
scalable, as shown experimentally in section 6. Also, in
Apriori-like algorithms which build higher length itemsets
from smaller ones, the relative ordering between the
itemsets is lost. In our method, we have considered only
the substrings of the phrases as keyword-sets and hence
the relative ordering is maintained preserving the
underlying semantic of the phrases.
D. Closed Frequent Keyword-sets as Topics
At this point, we have the frequent keyword-sets.
The papers that share the same keyword-set lie in the
same cluster. In our algorithm, we may derive non-closed
frequent keyword-sets as well. For example, ABCD and
ABC may have the same list of papers in their clusters. In
this case, we can remove ABC as it does not have any
information that ABCD does not have. Our topic should
consist of the maximal number of common keywords
present in all the papers in the cluster. Thus we need to
have closed frequent keyword-sets as topics. Notice that
we calculated the support of the frequent keyword-sets in
top-down fashion in a single scan in procedure 1. So we
cannot eliminate the non-closed keyword-sets in
procedure 1 itself.
To eliminate the non-closed frequent keyword-sets,
we iterate level-wise in the list of frequent keyword-sets.
We store the frequent keyword-sets in a level-wise
manner, with the number of keywords in the keyword-set
representing its level. For every keyword-set of length i,
we iterate over the list of keyword-sets of length (i+1) and
if i-length keyword-set is a substring of (i+1)-length
keyword-set and the support is same for both, we delete
the i-length keyword-set, as it is non-closed.
IV. CLUSTERING RESEARCH PAPERS BASED ON
TOPICS
Till now, we have closed frequent keyword-sets,
each representing a cluster of research papers. These topic
clusters are complete in the sense that we have the
maximal length keyword-set shared by all the research
papers represented by that topic. In the mapping Keyword
Set Paper List, we have the list of papers corresponding to
a topic. These sets of papers in the list form different
clusters. These clusters of research papers are overlapping
in nature as a paper may span more than one topic. These
clusters are also hierarchical in nature. The cluster
representing a broader topic is essentially a combination
of several clusters representing its sub-topics, which in
turn are a combination of the sub-sub-topic clusters and
so on. For example, databas system is a broad topic and
imag databas systems, distributed databas systems, etc.
are sub-topics lying within the broader topic databas
system. Each level of the hierarchy represents a different
level of data description, which facilitates the knowledge
discovery at various levels of abstraction.
Thus, we have used closed frequent keyword-sets
to form topics and used these topics as the similarity
measure to cluster the research papers.
V. RANKING OF RESEARCH PAPERS
For each topic we have a cluster of research papers
in which the topic lies. To find out which research papers
are of good quality, we have developed a time
independent, modified iterative PageRank algorithm. We
ranked the research papers based on citation network.
Each research paper is cited by a number of research
papers and there exists a well-defined graph structure
among the network of research papers.
The basic algorithm for ranking the research papers
based on citation network uses the two types of edges in a
graph: Outlinks and Inlinks.
Definition 5. Outlinks: From a given node N, link all the
nodes Ni that the node N cites.
Definition 6. Inlinks: To a given node N, link all the
nodes Nj that cite the node N.
These outlinks and inlinks will be used while calculating
the authoritative score [12] for each node. The procedure
2 uses modified iterative PageRank algorithm to calculate
the authoritative score for each node.
Procedure 2: Time-independent Modified Iterative PageRank
Algorithm
Require: Citation Network CN, Paper Year PY, Outlinks Count
OC, Paper Inlinks PI, Damping Factor θ
1: for each paper R in PY
2: year Y = PY[R]
3: Year Citation Count YCC[Y] += OC[R]
4: Year Paper Count YPC[Y] += 1
5: for each year Y in YCC
6: Average Year Citations Count AYCC[Y] = YCC[Y]/YPC[Y]
7: Initialize Paper Rank PR to 1.0 for each paper R
8: while true
9: flag = true
10: for each paper R in PR
11: Current Score CS = PR[R]
12: if R in PI
13: Inlinks List IL = PI[R]
14: New Score NS = 0.0
15: for each inlink I in IL
16: if I in PR
17: NS += PR[I]/OC[I]
18: year Y = PY[R]
19: NS = (1-θ) + θ*NS/AYCC[Y]
20: if CS is not equal to NS
21: flag = false
22: Updated Paper Rank UPR[R] = NS
23: if flag is equal to true
24: break
25: copy UPR to PR
26: clear UPR
27: Maximum Score MS = Maximum Score in PR
28: for each paper R in PR
29: PR[R] /= MS
In the procedure 2, we have the citation network CN as
input from which we create an Outlink Count OC for each
research paper which keeps track of the number of
outlinks corresponding to each research paper. We also
create Paper Inlinks PI which maps each paper to the list
of papers which are its inlinks. In the PageRank
algorithm, the damping factor θ is also used which is
required to prevent the scores of research papers that do
not have any inlinks from falling to zero. For the
experiments we set the damping factor to 0.85 [5] which
gave satisfactory result. In steps 1-6, we calculate the
value of metric Average Year Citations Count AYCC
which is the metric we introduce to counter the time
dependence of PageRank algorithm. This metric is the
average number of citations per paper in a particular
year which is a time dependent metric and directly
reflects the varying distribution of citations over the
years. We observe that this metric captures the time bias
against the newer papers well and has high values for
older papers and low values for newer ones. Considering
the year of publication of all the research papers, we pre-
compute the total number of citations for each year and
the number of research papers published in each year.
Using them, the average number of citations per paper for
each year is determined.
In line 7, each paper’s score is initialized to unity
and then the algorithm iteratively modifies the score
depending on the score of other papers that point towards
it. It stops when all the research paper’s scores converge,
i.e. become constant. Step 8 to step 26 is the iterative
calculation of the authoritative score for each research
paper R. The iteration stops when there is no change in
the score of any R. This is signified by no change in the
value of flag, set to true in step 9; flag is set to false in
step 16 if there is a change in the score of any paper R
during the update step. From step 10 to step 22, for each
research paper, a new authoritative score is calculated
based on the scores of the inlinks in the previous iteration.
The PageRank algorithm is based on the fact that the
quality of a node is equivalent to the summation of the
qualities of the nodes that point to it. Here, quality refers
to the score of the research paper. This fact is used in step
17 by dividing the inlink score by OC[I] which is the
number of outlinks of the inlink. This takes care of the
fact that if a research paper cites more than one paper, it
depicts that it has drawn inspiration from various sources
and hence its effect on the score of the paper it cites
should diminish by a factor equal to the number of paper
it cites. Step 19 modifies the score calculated above to
incorporate the damping factor θ. It also incorporates the
time-independence factor by using the average citations
per paper in a year metric AYCC[Y]. In step 23-24, if the
scores of none of the papers differ from the previous
iteration, the value of flag is unchanged from its true
value, and the loop breaks indicating the convergence of
the algorithms. In step 27 to 29, we normalize the scores
to scale down the scores within the range [0, 1]. Thus,
finally we get the authoritative score of each research
paper R based on the score of the research papers citing it.
VI. EXPERIMENTS AND RESULTS
A. Dataset
To show the results of our algorithms, we used the
DBLP XML Records available at http://dblp.uni-
trier.de/xml/ [7]. The DBLP dataset contains information
about various research papers from various fields
published over the years. This information includes the
title of the research paper, its author(s), the year of
publication, the conference of publication, a unique key
for each research paper and the keys of the research
papers the given research paper cites. It is to be noted that
the dataset used by us contained research papers with
citation information till the year 2010 only.
B. Data Preprocessing
The DBLP dataset also contains information that is
not useful in our algorithms. We need to pre-process the
dataset to extract only the information that we will use in
our algorithms. In data pre-processing, we extracted all
the research paper titles, year of publication and citations.
Also, all the keywords present in the titles of the research
papers were stemmed using the Porter's Stemming
Algorithm.
C. Results
We implemented our algorithms on the DBLP
dataset and discovered various interesting results. It
contains 16,32,442 research papers from various fields of
research. An objective and quantitative evaluation of the
result is difficult due to the lack of standard formal
measures for topic detection tasks. But, the list of topics
generated by our experiments on examination by field
experts and based on our observations match prevailing
topics in the dataset.
D. Topic Modeling
The topic modeling algorithm consisted of forming
closed frequent keyword-sets extracted from the phrases
present in titles of the research papers in the DBLP
dataset. Each of the closed frequent keyword-sets
represents a distinct topic.
We tested our algorithms on various values of
minimum support. Upon implementing the algorithms
with minimum support 100, we obtained 12,057 topic
clusters consisting of 5,476 1-length topics, 5,766 2-
length topics, 748 3-length topics, 62 4-length topics and
5 5-length topics. Based on their support, the top three n-
length topics were:
TABLE I: TOP 3 n-LENGTH TOPICS BASED ON SUPPORT
n-length Top Three Topics
1-length system model network
2-length neural network real time sensor
network
3-length wireless sensor
network
support
vector
machin
ad hoc
network
4-length mobil ad hoc
network
wireless ad
hoc
network
content
base imag
retriev
5-length radial basi
function neural
network
ieee 802 11
wirelss lan
low density
pariti check
code
D.1. Top Research Papers in a Topic Cluster
For each topic T, we have the ranked list of
research papers as determined by our algorithms. This can
be very useful to researchers studying a given topic T.
Following is the list of the top three research papers
for some of the prominent topics of research.
TABLE II: TOP 3 RESEARCH PAPERS FOR SOME PROMINENT
TOPICS ALONG WITH THE TOPIC CLUSTER SIZE
Topic Cluster Size Top 3 Research Papers
algorithm
81506
Introduction to Algorithms.
Graph-Based Algorithms for
Boolean Function
Manipulation.
Fast Algorithms for Mining
Association Rules in Large
Databases.
associ rul
1737
Mining Association Rules
between Sets of Items in
Large Databases.
Fast Algorithms for Mining
Association Rules in Large
Databases.
Fast Discovery of
Association Rules.
onlin social
network
104
A familiar face(book): profile
elements as signals in an
online social network.
Information revelation and
privacy in online social
networks.
Measurement and analysis of
online social networks.
neural
network
19178
Neural Networks and the
Bias/Variance Dilemma.
Image processing with neural
networks - a review.
Evolving Neural Network
through Augmenting
Topologies.
xml databas
226
TIMBER: A native XML
database.
Efficient Keyword Search for
Smallest LCAs in XML
Databases.
Querying Structured Text in
an XML Database.
The top research papers for the t
to match the most popular research pape
as determined by field experts. Also
found to be considerably consistent ir
cluster size or the age of the topics.
E. Performance
To evaluate the performance of o
varied minimum support and plotted the
algorithm to run. The algorithm bui
keyword-sets and eliminates the non-clos
The following graph shows this variation
Figure 2. Graph showing minimum support versu
algorithm to run
Above algorithm was run on a 3G
2.10GHz Intel® Core™ 2 Duo CPU.
VII. APPLICATIONS
Our algorithms have a variety o
various fields of research. Following
applications areas:
• Topic Search System: This system
the top ranked papers of a g
retrieval of papers on year-wise
be given as an option.
• Topic Ranking System: Using
research papers for the top
authoritative scores, we would
system that would compare the to
a ranked list of topics.
• Evolution of Topics: Evolution of
tool for new researchers. He can i
the emerging fields of research by
in the currently popular topics.
• Recommendation Systems: Extend
to authors and conferences based
build a recommendation system. B
topics of interests of the a
recommend those topics to an
other similar authors have worke
topics were found
ers from that topic
our results were
rrespective of the
our algorithm, we
time taken by our
ilds the frequent
sed frequent ones.
n.
us time taken by the
GB machine with
S
of applications in
are some of the
m would retrieve
given topic. The
granularity could
the cluster of
pics and their
to like build a
opics and produce
f topics is a useful
intuitively deduce
y seeing the trends
ding the clustering
on topics, we can
By comparing the
authors, we can
author on which
ed. This similarity
between authors could be
number of common topics
them. Similarly, we can
conferences of interest to the
• Finding Landmark Papers:
contains all the research pa
topic, we can determine the
for the topic by observing the
the topic. A sudden peak
suggest the presence of an im
topic. These important paper
by comparing the authoritativ
in the corresponding topic
classified as Landmark Pape
VIII. CONCLUSIONS AND
In this paper, we proposed
topics and cluster research papers
topics were identified by form
keyword-sets as proposed by our alg
better than traditional approaches
proposed a method to produce the
papers within a topic cluster. We a
topic modeling as well as the
algorithms on varying the minimum
As mentioned above, our alg
of applications. In future, we would
algorithms to build systems for topi
and recommendations. We would
statistical approaches for topic co
other domains like web site
clustering, etc. in which our algorith
IX. REFEREN
[1] R. Agarwal, T. Imielinski, an
Association Rules between S
Databases,” Proceedings
SIGMOD Conference, 1993.
[2] R. Agarwal, and R. Srikant,
Mining Association Rules,”
20th VLDB Conference, 199
[3] F. Beil, M. Ester, and X.
Based Text Clustering,” P
International Conference on
and Data Mining (KDD), 200
[4] D. M. Blei, and J. D. Laffe
Models,” NIPS, 2005.
[5] S. Brin, and L. Page, “The
Scale Hypertextual Web
Proceedings of the 7th Intern
World Wide Web, 1998.
[6] Z. Chong, L. Yansheng, Z
“FICW: Frequent Itemset B
with Window Constraint,”
Natural Sciences, Vol: 11, N
2006.
[7] The DBLP Computer S
http://dblp.uni-trier.de/
based on a threshold
of research shared by
also recommend the
authors.
Since a topic cluster
apers published on the
most important papers
e graph of evolution of
in the graph would
mportant paper for the
rs could be recognized
ve scores of the papers
cluster and then be
rs.
FUTURE WORK
d a method to derive
into these topics. The
ming closed frequent
gorithms, which works
like Apriori. We also
ranked list of research
analyzed the results of
performance of our
m support parameter.
gorithms have a variety
d like to implement our
ic search, topic ranking
also like to examine
orrelation and explore
clustering, document
hms can be applied.
NCES
nd A. Swami, “Mining
Sets of Items in Large
of the 1993 ACM
, “Fast Algorithms for
” Proceedings of the
94.
Xu, “Frequent Term-
roceeding of the 8th
Knowledge Discovery
02.
rty, “Correlated Topic
Anatomy of a Large-
b Search Engine,”
national Conference on
Z. Lei, and H. Rong,
Based Text Clustering
” Wuhan Journal of
No: 5, pp: 1345-1351,
Science Bibliography.
[8] E. Erosheva, S. Fienberg, and J. Lafferty, “Mixed-
membership Models of Scientific Publications,”
Proceedings of the National Academy of Sciences,
2004.
[9] X. Geng, and J. Wang, “Toward theme
development analysis with topic clustering,”
Proceedings of the 1st International Conference on
Advanced Computer Theory and Engineering,
2008.
[10] T. I. Griffiths, and M. Steyvers, “Finding Scientific
Topics,” Proceedings of the National Academy of
Sciences, 2004.
[11] Y. Jo, C. Lagoze, and C. L. Giles, ”Detecting
Research Topics via the Correlation between the
Graphs and Texts,” Proceedings of the 13th ACM
SIGKDD international conference on Knowledge
discovery and data mining, 2007.
[12] J. Klienberg, “Authoritative sources in a
hyperlinked environment,” Proceedings of the 9th
Annual ACM-SIAM Symposium on Discrete
Algorithms, 1998.
[13] J. Kleinberg, “Bursty and Hierarchical Structure in
Streams,” Proceedings of SIGKDD, 2002.
[14] S. M. Krishna, and S. D. Bhavani, “An Efficient
Approach for Text Clustering Based on Frequent
Itemsets,” European Journal of Scientific Research,
2010.
[15] C.Y. Lin, and E. Hovy, “The Automated
Acquisition of Topic Signatures for Text
Summarization,” Proceedings of the COLING
Conference, 2002.
[16] E. Lloret, “Topic Detection and Segmentation in
Automatic Text Summarization,”
http://www.dlsi.ua.es/~elloret/publications/SumTo
pics.pdf, 2009.
[17] G. S. Mann, D. Mimmo, and A. McCallum,
“Bibliometric Impact Measures Leveraging Topic
Analysis,” JCDL, 2006.
[18] A. McCallum, A. Corrada-Emmanuel, and X.
Wang, “The Author-recipient-topic Model for
Topic and Role Discovery in Social Networks:
Experiments with Enron and Academic E-mail,”
Technical Report, 2004.
[19] Q. Mei, and C. Zhai, “Discovery Evolutionary
Theme Patterns from Text – An Exploration of
Temporal Text Mining,” Proceedings of SIGKDD,
2005.
[20] N. Pasquier, Y. Bastide, R. Taoull, and L. Lakhal,
“Efficient Mining of Association Rules Using
Closed Itemset Lattices,” Information Systems,
1999.
[21] M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. I.
Griffiths, “Probabilistic Author-topic Models for
Information Discovery,” Proceedings of SIGKDD,
2004.
[22] X. Wang, and A. McCallum, “Topics over Time: A
Non-Markov Continuous-time Model for Topical
Trends,” Proceedings of SIGKDD, 2006.
[23] C. Wartena, and R. Brussee, “Topic Detection by
Clustering Keywords,” Proceedings of the 19th
International Conference on Database and Expert
Systems Applications, 2008.
[24] D. Zhou, E. Manavoglu, J. Li, C. L. Giles, and H.
Zha, “Probabilistic Models for Discovering E-
communities,” Proceedings of WWW, 2006.
[25] L. Zhuang, and H. Dai, “A Maximal Frequent
Itemset Approach for Web Document Clustering,”
Proceedings of the 4th International Conference on
Computer and Information Technology, 2004.
<<
/ASCII85EncodePages false
/AllowTransparency false
/AutoPositionEPSFiles true
/AutoRotatePages /None
/Binding /Left
/CalGrayProfile (Gray Gamma 2.2)
/CalRGBProfile (sRGB IEC61966-2.1)
/CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2)
/sRGBProfile (sRGB IEC61966-2.1)
/CannotEmbedFontPolicy /Warning
/CompatibilityLevel 1.7
/CompressObjects /Off
/CompressPages true
/ConvertImagesToIndexed true
/PassThroughJPEGImages true
/CreateJobTicket false
/DefaultRenderingIntent /Default
/DetectBlends true
/DetectCurves 0.0000
/ColorConversionStrategy /LeaveColorUnchanged
/DoThumbnails true
/EmbedAllFonts true
/EmbedOpenType false
/ParseICCProfilesInComments true
/EmbedJobOptions true
/DSCReportingLevel 0
/EmitDSCWarnings false
/EndPage -1
/ImageMemory 1048576
/LockDistillerParams true
/MaxSubsetPct 100
/Optimize true
/OPM 0
/ParseDSCComments false
/ParseDSCCommentsForDocInfo false
/PreserveCopyPage true
/PreserveDICMYKValues true
/PreserveEPSInfo false
/PreserveFlatness true
/PreserveHalftoneInfo true
/PreserveOPIComments false
/PreserveOverprintSettings true
/StartPage 1
/SubsetFonts true
/TransferFunctionInfo /Remove
/UCRandBGInfo /Preserve
/UsePrologue false
/ColorSettingsFile ()
/AlwaysEmbed [ true
/AbadiMT-CondensedLight
/ACaslon-Italic
/ACaslon-Regular
/ACaslon-Semibold
/ACaslon-SemiboldItalic
/AdobeArabic-Bold
/AdobeArabic-BoldItalic
/AdobeArabic-Italic
/AdobeArabic-Regular
/AdobeHebrew-Bold
/AdobeHebrew-BoldItalic
/AdobeHebrew-Italic
/AdobeHebrew-Regular
/AdobeHeitiStd-Regular
/AdobeMingStd-Light
/AdobeMyungjoStd-Medium
/AdobePiStd
/AdobeSansMM
/AdobeSerifMM
/AdobeSongStd-Light
/AdobeThai-Bold
/AdobeThai-BoldItalic
/AdobeThai-Italic
/AdobeThai-Regular
/AGaramond-Bold
/AGaramond-BoldItalic
/AGaramond-Italic
/AGaramond-Regular
/AGaramond-Semibold
/AGaramond-SemiboldItalic
/AgencyFB-Bold
/AgencyFB-Reg
/AGOldFace-Outline
/AharoniBold
/Algerian
/Americana
/Americana-ExtraBold
/AndaleMono
/AndaleMonoIPA
/AngsanaNew
/AngsanaNew-Bold
/AngsanaNew-BoldItalic
/AngsanaNew-Italic
/AngsanaUPC
/AngsanaUPC-Bold
/AngsanaUPC-BoldItalic
/AngsanaUPC-Italic
/Anna
/ArialAlternative
/ArialAlternativeSymbol
/Arial-Black
/Arial-BlackItalic
/Arial-BoldItalicMT
/Arial-BoldMT
/Arial-ItalicMT
/ArialMT
/ArialMT-Black
/ArialNarrow
/ArialNarrow-Bold
/ArialNarrow-BoldItalic
/ArialNarrow-Italic
/ArialRoundedMTBold
/ArialUnicodeMS
/ArrusBT-Bold
/ArrusBT-BoldItalic
/ArrusBT-Italic
/ArrusBT-Roman
/AvantGarde-Book
/AvantGarde-BookOblique
/AvantGarde-Demi
/AvantGarde-DemiOblique
/AvantGardeITCbyBT-Book
/AvantGardeITCbyBT-BookOblique
/BakerSignet
/BankGothicBT-Medium
/Barmeno-Bold
/Barmeno-ExtraBold
/Barmeno-Medium
/Barmeno-Regular
/Baskerville
/BaskervilleBE-Italic
/BaskervilleBE-Medium
/BaskervilleBE-MediumItalic
/BaskervilleBE-Regular
/Baskerville-Bold
/Baskerville-BoldItalic
/Baskerville-Italic
/BaskOldFace
/Batang
/BatangChe
/Bauhaus93
/Bellevue
/BellGothicStd-Black
/BellGothicStd-Bold
/BellGothicStd-Light
/BellMT
/BellMTBold
/BellMTItalic
/BerlingAntiqua-Bold
/BerlingAntiqua-BoldItalic
/BerlingAntiqua-Italic
/BerlingAntiqua-Roman
/BerlinSansFB-Bold
/BerlinSansFBDemi-Bold
/BerlinSansFB-Reg
/BernardMT-Condensed
/BernhardModernBT-Bold
/BernhardModernBT-BoldItalic
/BernhardModernBT-Italic
/BernhardModernBT-Roman
/BiffoMT
/BinnerD
/BinnerGothic
/BlackadderITC-Regular
/Blackoak
/Bodoni
/Bodoni-Bold
/Bodoni-BoldItalic
/Bodoni-Italic
/BodoniMT
/BodoniMTBlack
/BodoniMTBlack-Italic
/BodoniMT-Bold
/BodoniMT-BoldItalic
/BodoniMTCondensed
/BodoniMTCondensed-Bold
/BodoniMTCondensed-BoldItalic
/BodoniMTCondensed-Italic
/BodoniMT-Italic
/BodoniMTPosterCompressed
/Bodoni-Poster
/Bodoni-PosterCompressed
/BookAntiqua
/BookAntiqua-Bold
/BookAntiqua-BoldItalic
/BookAntiqua-Italic
/Bookman-Demi
/Bookman-DemiItalic
/Bookman-Light
/Bookman-LightItalic
/BookmanOldStyle
/BookmanOldStyle-Bold
/BookmanOldStyle-BoldItalic
/BookmanOldStyle-Italic
/BookshelfSymbolOne-Regular
/BookshelfSymbolSeven
/BookshelfSymbolThree-Regular
/BookshelfSymbolTwo-Regular
/Botanical
/Boton-Italic
/Boton-Medium
/Boton-MediumItalic
/Boton-Regular
/Boulevard
/BradleyHandITC
/Braggadocio
/BritannicBold
/Broadway
/BrowalliaNew
/BrowalliaNew-Bold
/BrowalliaNew-BoldItalic
/BrowalliaNew-Italic
/BrowalliaUPC
/BrowalliaUPC-Bold
/BrowalliaUPC-BoldItalic
/BrowalliaUPC-Italic
/BrushScript
/BrushScriptMT
/CaflischScript-Bold
/CaflischScript-Regular
/Calibri
/Calibri-Bold
/Calibri-BoldItalic
/Calibri-Italic
/CalifornianFB-Bold
/CalifornianFB-Italic
/CalifornianFB-Reg
/CalisMTBol
/CalistoMT
/CalistoMT-BoldItalic
/CalistoMT-Italic
/Cambria
/Cambria-Bold
/Cambria-BoldItalic
/Cambria-Italic
/CambriaMath
/Candara
/Candara-Bold
/Candara-BoldItalic
/Candara-Italic
/Carta
/CaslonOpenfaceBT-Regular
/Castellar
/CastellarMT
/Centaur
/Centaur-Italic
/Century
/CenturyGothic
/CenturyGothic-Bold
/CenturyGothic-BoldItalic
/CenturyGothic-Italic
/CenturySchL-Bold
/CenturySchL-BoldItal
/CenturySchL-Ital
/CenturySchL-Roma
/CenturySchoolbook
/CenturySchoolbook-Bold
/CenturySchoolbook-BoldItalic
/CenturySchoolbook-Italic
/CGTimes-Bold
/CGTimes-BoldItalic
/CGTimes-Italic
/CGTimes-Regular
/CharterBT-Bold
/CharterBT-BoldItalic
/CharterBT-Italic
/CharterBT-Roman
/CheltenhamITCbyBT-Bold
/CheltenhamITCbyBT-BoldItalic
/CheltenhamITCbyBT-Book
/CheltenhamITCbyBT-BookItalic
/Chiller-Regular
/CMB10
/CMBSY10
/CMBSY5
/CMBSY6
/CMBSY7
/CMBSY8
/CMBSY9
/CMBX10
/CMBX12
/CMBX5
/CMBX6
/CMBX7
/CMBX8
/CMBX9
/CMBXSL10
/CMBXTI10
/CMCSC10
/CMCSC8
/CMCSC9
/CMDUNH10
/CMEX10
/CMEX7
/CMEX8
/CMEX9
/CMFF10
/CMFI10
/CMFIB8
/CMINCH
/CMITT10
/CMMI10
/CMMI12
/CMMI5
/CMMI6
/CMMI7
/CMMI8
/CMMI9
/CMMIB10
/CMMIB5
/CMMIB6
/CMMIB7
/CMMIB8
/CMMIB9
/CMR10
/CMR12
/CMR17
/CMR5
/CMR6
/CMR7
/CMR8
/CMR9
/CMSL10
/CMSL12
/CMSL8
/CMSL9
/CMSLTT10
/CMSS10
/CMSS12
/CMSS17
/CMSS8
/CMSS9
/CMSSBX10
/CMSSDC10
/CMSSI10
/CMSSI12
/CMSSI17
/CMSSI8
/CMSSI9
/CMSSQ8
/CMSSQI8
/CMSY10
/CMSY5
/CMSY6
/CMSY7
/CMSY8
/CMSY9
/CMTCSC10
/CMTEX10
/CMTEX8
/CMTEX9
/CMTI10
/CMTI12
/CMTI7
/CMTI8
/CMTI9
/CMTT10
/CMTT12
/CMTT8
/CMTT9
/CMU10
/CMVTT10
/ColonnaMT
/Colossalis-Bold
/ComicSansMS
/ComicSansMS-Bold
/Consolas
/Consolas-Bold
/Consolas-BoldItalic
/Consolas-Italic
/Constantia
/Constantia-Bold
/Constantia-BoldItalic
/Constantia-Italic
/CooperBlack
/CopperplateGothic-Bold
/CopperplateGothic-Light
/Copperplate-ThirtyThreeBC
/Corbel
/Corbel-Bold
/Corbel-BoldItalic
/Corbel-Italic
/CordiaNew
/CordiaNew-Bold
/CordiaNew-BoldItalic
/CordiaNew-Italic
/CordiaUPC
/CordiaUPC-Bold
/CordiaUPC-BoldItalic
/CordiaUPC-Italic
/Courier
/Courier-Bold
/Courier-BoldOblique
/CourierNewPS-BoldItalicMT
/CourierNewPS-BoldMT
/CourierNewPS-ItalicMT
/CourierNewPSMT
/Courier-Oblique
/CourierStd
/CourierStd-Bold
/CourierStd-BoldOblique
/CourierStd-Oblique
/CourierX-Bold
/CourierX-BoldOblique
/CourierX-Oblique
/CourierX-Regular
/CreepyRegular
/CurlzMT
/David-Bold
/David-Reg
/DavidTransparent
/Desdemona
/DilleniaUPC
/DilleniaUPCBold
/DilleniaUPCBoldItalic
/DilleniaUPCItalic
/Dingbats
/DomCasual
/Dotum
/DotumChe
/EdwardianScriptITC
/Elephant-Italic
/Elephant-Regular
/EngraversGothicBT-Regular
/EngraversMT
/EraserDust
/ErasITC-Bold
/ErasITC-Demi
/ErasITC-Light
/ErasITC-Medium
/ErieBlackPSMT
/ErieLightPSMT
/EriePSMT
/EstrangeloEdessa
/Euclid
/Euclid-Bold
/Euclid-BoldItalic
/EuclidExtra
/EuclidExtra-Bold
/EuclidFraktur
/EuclidFraktur-Bold
/Euclid-Italic
/EuclidMathOne
/EuclidMathOne-Bold
/EuclidMathTwo
/EuclidMathTwo-Bold
/EuclidSymbol
/EuclidSymbol-Bold
/EuclidSymbol-BoldItalic
/EuclidSymbol-Italic
/EucrosiaUPC
/EucrosiaUPCBold
/EucrosiaUPCBoldItalic
/EucrosiaUPCItalic
/EUEX10
/EUEX7
/EUEX8
/EUEX9
/EUFB10
/EUFB5
/EUFB7
/EUFM10
/EUFM5
/EUFM7
/EURB10
/EURB5
/EURB7
/EURM10
/EURM5
/EURM7
/EuroMono-Bold
/EuroMono-BoldItalic
/EuroMono-Italic
/EuroMono-Regular
/EuroSans-Bold
/EuroSans-BoldItalic
/EuroSans-Italic
/EuroSans-Regular
/EuroSerif-Bold
/EuroSerif-BoldItalic
/EuroSerif-Italic
/EuroSerif-Regular
/EuroSig
/EUSB10
/EUSB5
/EUSB7
/EUSM10
/EUSM5
/EUSM7
/FelixTitlingMT
/Fences
/FencesPlain
/FigaroMT
/FixedMiriamTransparent
/FootlightMTLight
/Formata-Italic
/Formata-Medium
/Formata-MediumItalic
/Formata-Regular
/ForteMT
/FranklinGothic-Book
/FranklinGothic-BookItalic
/FranklinGothic-Demi
/FranklinGothic-DemiCond
/FranklinGothic-DemiItalic
/FranklinGothic-Heavy
/FranklinGothic-HeavyItalic
/FranklinGothicITCbyBT-Book
/FranklinGothicITCbyBT-BookItal
/FranklinGothicITCbyBT-Demi
/FranklinGothicITCbyBT-DemiItal
/FranklinGothic-Medium
/FranklinGothic-MediumCond
/FranklinGothic-MediumItalic
/FrankRuehl
/FreesiaUPC
/FreesiaUPCBold
/FreesiaUPCBoldItalic
/FreesiaUPCItalic
/FreestyleScript-Regular
/FrenchScriptMT
/Frutiger-Black
/Frutiger-BlackCn
/Frutiger-BlackItalic
/Frutiger-Bold
/Frutiger-BoldCn
/Frutiger-BoldItalic
/Frutiger-Cn
/Frutiger-ExtraBlackCn
/Frutiger-Italic
/Frutiger-Light
/Frutiger-LightCn
/Frutiger-LightItalic
/Frutiger-Roman
/Frutiger-UltraBlack
/Futura-Bold
/Futura-BoldOblique
/Futura-Book
/Futura-BookOblique
/FuturaBT-Bold
/FuturaBT-BoldItalic
/FuturaBT-Book
/FuturaBT-BookItalic
/FuturaBT-Medium
/FuturaBT-MediumItalic
/Futura-Light
/Futura-LightOblique
/GalliardITCbyBT-Bold
/GalliardITCbyBT-BoldItalic
/GalliardITCbyBT-Italic
/GalliardITCbyBT-Roman
/Garamond
/Garamond-Bold
/Garamond-BoldCondensed
/Garamond-BoldCondensedItalic
/Garamond-BoldItalic
/Garamond-BookCondensed
/Garamond-BookCondensedItalic
/Garamond-Italic
/Garamond-LightCondensed
/Garamond-LightCondensedItalic
/Gautami
/GeometricSlab703BT-Light
/GeometricSlab703BT-LightItalic
/Georgia
/Georgia-Bold
/Georgia-BoldItalic
/Georgia-Italic
/GeorgiaRef
/Giddyup
/Giddyup-Thangs
/Gigi-Regular
/GillSans
/GillSans-Bold
/GillSans-BoldItalic
/GillSans-Condensed
/GillSans-CondensedBold
/GillSans-Italic
/GillSans-Light
/GillSans-LightItalic
/GillSansMT
/GillSansMT-Bold
/GillSansMT-BoldItalic
/GillSansMT-Condensed
/GillSansMT-ExtraCondensedBold
/GillSansMT-Italic
/GillSans-UltraBold
/GillSans-UltraBoldCondensed
/GloucesterMT-ExtraCondensed
/Gothic-Thirteen
/GoudyOldStyleBT-Bold
/GoudyOldStyleBT-BoldItalic
/GoudyOldStyleBT-Italic
/GoudyOldStyleBT-Roman
/GoudyOldStyleT-Bold
/GoudyOldStyleT-Italic
/GoudyOldStyleT-Regular
/GoudyStout
/GoudyTextMT-LombardicCapitals
/GSIDefaultSymbols
/Gulim
/GulimChe
/Gungsuh
/GungsuhChe
/Haettenschweiler
/HarlowSolid
/Harrington
/Helvetica
/Helvetica-Black
/Helvetica-BlackOblique
/Helvetica-Bold
/Helvetica-BoldOblique
/Helvetica-Condensed
/Helvetica-Condensed-Black
/Helvetica-Condensed-BlackObl
/Helvetica-Condensed-Bold
/Helvetica-Condensed-BoldObl
/Helvetica-Condensed-Light
/Helvetica-Condensed-LightObl
/Helvetica-Condensed-Oblique
/Helvetica-Fraction
/Helvetica-Narrow
/Helvetica-Narrow-Bold
/Helvetica-Narrow-BoldOblique
/Helvetica-Narrow-Oblique
/Helvetica-Oblique
/HighTowerText-Italic
/HighTowerText-Reg
/Humanist521BT-BoldCondensed
/Humanist521BT-Light
/Humanist521BT-LightItalic
/Humanist521BT-RomanCondensed
/Imago-ExtraBold
/Impact
/ImprintMT-Shadow
/InformalRoman-Regular
/IrisUPC
/IrisUPCBold
/IrisUPCBoldItalic
/IrisUPCItalic
/Ironwood
/ItcEras-Medium
/ItcKabel-Bold
/ItcKabel-Book
/ItcKabel-Demi
/ItcKabel-Medium
/ItcKabel-Ultra
/JasmineUPC
/JasmineUPC-Bold
/JasmineUPC-BoldItalic
/JasmineUPC-Italic
/JoannaMT
/JoannaMT-Italic
/Jokerman-Regular
/JuiceITC-Regular
/Kartika
/Kaufmann
/KaufmannBT-Bold
/KaufmannBT-Regular
/KidTYPEPaint
/KinoMT
/KodchiangUPC
/KodchiangUPC-Bold
/KodchiangUPC-BoldItalic
/KodchiangUPC-Italic
/KorinnaITCbyBT-Regular
/KozGoProVI-Medium
/KozMinProVI-Regular
/KristenITC-Regular
/KunstlerScript
/Latha
/LatinWide
/LetterGothic
/LetterGothic-Bold
/LetterGothic-BoldOblique
/LetterGothic-BoldSlanted
/LetterGothicMT
/LetterGothicMT-Bold
/LetterGothicMT-BoldOblique
/LetterGothicMT-Oblique
/LetterGothic-Slanted
/LetterGothicStd
/LetterGothicStd-Bold
/LetterGothicStd-BoldSlanted
/LetterGothicStd-Slanted
/LevenimMT
/LevenimMTBold
/LilyUPC
/LilyUPCBold
/LilyUPCBoldItalic
/LilyUPCItalic
/Lithos-Black
/Lithos-Regular
/LotusWPBox-Roman
/LotusWPIcon-Roman
/LotusWPIntA-Roman
/LotusWPIntB-Roman
/LotusWPType-Roman
/LucidaBright
/LucidaBright-Demi
/LucidaBright-DemiItalic
/LucidaBright-Italic
/LucidaCalligraphy-Italic
/LucidaConsole
/LucidaFax
/LucidaFax-Demi
/LucidaFax-DemiItalic
/LucidaFax-Italic
/LucidaHandwriting-Italic
/LucidaSans
/LucidaSans-Demi
/LucidaSans-DemiItalic
/LucidaSans-Italic
/LucidaSans-Typewriter
/LucidaSans-TypewriterBold
/LucidaSans-TypewriterBoldOblique
/LucidaSans-TypewriterOblique
/LucidaSansUnicode
/Lydian
/Magneto-Bold
/MaiandraGD-Regular
/Mangal-Regular
/Map-Symbols
/MathA
/MathB
/MathC
/Mathematica1
/Mathematica1-Bold
/Mathematica1Mono
/Mathematica1Mono-Bold
/Mathematica2
/Mathematica2-Bold
/Mathematica2Mono
/Mathematica2Mono-Bold
/Mathematica3
/Mathematica3-Bold
/Mathematica3Mono
/Mathematica3Mono-Bold
/Mathematica4
/Mathematica4-Bold
/Mathematica4Mono
/Mathematica4Mono-Bold
/Mathematica5
/Mathematica5-Bold
/Mathematica5Mono
/Mathematica5Mono-Bold
/Mathematica6
/Mathematica6Bold
/Mathematica6Mono
/Mathematica6MonoBold
/Mathematica7
/Mathematica7Bold
/Mathematica7Mono
/Mathematica7MonoBold
/MatisseITC-Regular
/MaturaMTScriptCapitals
/Mesquite
/Mezz-Black
/Mezz-Regular
/MICR
/MicrosoftSansSerif
/MingLiU
/Minion-BoldCondensed
/Minion-BoldCondensedItalic
/Minion-Condensed
/Minion-CondensedItalic
/Minion-Ornaments
/MinionPro-Bold
/MinionPro-BoldIt
/MinionPro-It
/MinionPro-Regular
/MinionPro-Semibold
/MinionPro-SemiboldIt
/Miriam
/MiriamFixed
/MiriamTransparent
/Mistral
/Modern-Regular
/MonotypeCorsiva
/MonotypeSorts
/MSAM10
/MSAM5
/MSAM6
/MSAM7
/MSAM8
/MSAM9
/MSBM10
/MSBM5
/MSBM6
/MSBM7
/MSBM8
/MSBM9
/MS-Gothic
/MSHei
/MSLineDrawPSMT
/MS-Mincho
/MSOutlook
/MS-PGothic
/MS-PMincho
/MSReference1
/MSReference2
/MSReferenceSansSerif
/MSReferenceSansSerif-Bold
/MSReferenceSansSerif-BoldItalic
/MSReferenceSansSerif-Italic
/MSReferenceSerif
/MSReferenceSerif-Bold
/MSReferenceSerif-BoldItalic
/MSReferenceSerif-Italic
/MSReferenceSpecialty
/MSSong
/MS-UIGothic
/MT-Extra
/MT-Symbol
/MT-Symbol-Italic
/MVBoli
/Myriad-Bold
/Myriad-BoldItalic
/Myriad-Italic
/MyriadPro-Black
/MyriadPro-BlackIt
/MyriadPro-Bold
/MyriadPro-BoldIt
/MyriadPro-It
/MyriadPro-Light
/MyriadPro-LightIt
/MyriadPro-Regular
/MyriadPro-Semibold
/MyriadPro-SemiboldIt
/Myriad-Roman
/Narkisim
/NewCenturySchlbk-Bold
/NewCenturySchlbk-BoldItalic
/NewCenturySchlbk-Italic
/NewCenturySchlbk-Roman
/NewMilleniumSchlbk-BoldItalicSH
/NewsGothic
/NewsGothic-Bold
/NewsGothicBT-Bold
/NewsGothicBT-BoldItalic
/NewsGothicBT-Italic
/NewsGothicBT-Roman
/NewsGothic-Condensed
/NewsGothic-Italic
/NewsGothicMT
/NewsGothicMT-Bold
/NewsGothicMT-Italic
/NiagaraEngraved-Reg
/NiagaraSolid-Reg
/NimbusMonL-Bold
/NimbusMonL-BoldObli
/NimbusMonL-Regu
/NimbusMonL-ReguObli
/NimbusRomNo9L-Medi
/NimbusRomNo9L-MediItal
/NimbusRomNo9L-Regu
/NimbusRomNo9L-ReguItal
/NimbusSanL-Bold
/NimbusSanL-BoldCond
/NimbusSanL-BoldCondItal
/NimbusSanL-BoldItal
/NimbusSanL-Regu
/NimbusSanL-ReguCond
/NimbusSanL-ReguCondItal
/NimbusSanL-ReguItal
/Nimrod
/Nimrod-Bold
/Nimrod-BoldItalic
/Nimrod-Italic
/NSimSun
/Nueva-BoldExtended
/Nueva-BoldExtendedItalic
/Nueva-Italic
/Nueva-Roman
/NuptialScript
/OCRA
/OCRA-Alternate
/OCRAExtended
/OCRB
/OCRB-Alternate
/OfficinaSans-Bold
/OfficinaSans-BoldItalic
/OfficinaSans-Book
/OfficinaSans-BookItalic
/OfficinaSerif-Bold
/OfficinaSerif-BoldItalic
/OfficinaSerif-Book
/OfficinaSerif-BookItalic
/OldEnglishTextMT
/Onyx
/OnyxBT-Regular
/OzHandicraftBT-Roman
/PalaceScriptMT
/Palatino-Bold
/Palatino-BoldItalic
/Palatino-Italic
/PalatinoLinotype-Bold
/PalatinoLinotype-BoldItalic
/PalatinoLinotype-Italic
/PalatinoLinotype-Roman
/Palatino-Roman
/PapyrusPlain
/Papyrus-Regular
/Parchment-Regular
/Parisian
/ParkAvenue
/Penumbra-SemiboldFlare
/Penumbra-SemiboldSans
/Penumbra-SemiboldSerif
/PepitaMT
/Perpetua
/Perpetua-Bold
/Perpetua-BoldItalic
/Perpetua-Italic
/PerpetuaTitlingMT-Bold
/PerpetuaTitlingMT-Light
/PhotinaCasualBlack
/Playbill
/PMingLiU
/Poetica-SuppOrnaments
/PoorRichard-Regular
/PopplLaudatio-Italic
/PopplLaudatio-Medium
/PopplLaudatio-MediumItalic
/PopplLaudatio-Regular
/PrestigeElite
/Pristina-Regular
/PTBarnumBT-Regular
/Raavi
/RageItalic
/Ravie
/RefSpecialty
/Ribbon131BT-Bold
/Rockwell
/Rockwell-Bold
/Rockwell-BoldItalic
/Rockwell-Condensed
/Rockwell-CondensedBold
/Rockwell-ExtraBold
/Rockwell-Italic
/Rockwell-Light
/Rockwell-LightItalic
/Rod
/RodTransparent
/RunicMT-Condensed
/Sanvito-Light
/Sanvito-Roman
/ScriptC
/ScriptMTBold
/SegoeUI
/SegoeUI-Bold
/SegoeUI-BoldItalic
/SegoeUI-Italic
/Serpentine-BoldOblique
/ShelleyVolanteBT-Regular
/ShowcardGothic-Reg
/Shruti
/SimHei
/SimSun
/SnapITC-Regular
/StandardSymL
/Stencil
/StoneSans
/StoneSans-Bold
/StoneSans-BoldItalic
/StoneSans-Italic
/StoneSans-Semibold
/StoneSans-SemiboldItalic
/Stop
/Swiss721BT-BlackExtended
/Sylfaen
/Symbol
/SymbolMT
/Tahoma
/Tahoma-Bold
/Tci1
/Tci1Bold
/Tci1BoldItalic
/Tci1Italic
/Tci2
/Tci2Bold
/Tci2BoldItalic
/Tci2Italic
/Tci3
/Tci3Bold
/Tci3BoldItalic
/Tci3Italic
/Tci4
/Tci4Bold
/Tci4BoldItalic
/Tci4Italic
/TechnicalItalic
/TechnicalPlain
/Tekton
/Tekton-Bold
/TektonMM
/Tempo-HeavyCondensed
/Tempo-HeavyCondensedItalic
/TempusSansITC
/Times-Bold
/Times-BoldItalic
/Times-BoldItalicOsF
/Times-BoldSC
/Times-ExtraBold
/Times-Italic
/Times-ItalicOsF
/TimesNewRomanMT-ExtraBold
/TimesNewRomanPS-BoldItalicMT
/TimesNewRomanPS-BoldMT
/TimesNewRomanPS-ItalicMT
/TimesNewRomanPSMT
/Times-Roman
/Times-RomanSC
/Trajan-Bold
/Trebuchet-BoldItalic
/TrebuchetMS
/TrebuchetMS-Bold
/TrebuchetMS-Italic
/Tunga-Regular
/TwCenMT-Bold
/TwCenMT-BoldItalic
/TwCenMT-Condensed
/TwCenMT-CondensedBold
/TwCenMT-CondensedExtraBold
/TwCenMT-CondensedMedium
/TwCenMT-Italic
/TwCenMT-Regular
/Univers-Bold
/Univers-BoldItalic
/UniversCondensed-Bold
/UniversCondensed-BoldItalic
/UniversCondensed-Medium
/UniversCondensed-MediumItalic
/Univers-Medium
/Univers-MediumItalic
/URWBookmanL-DemiBold
/URWBookmanL-DemiBoldItal
/URWBookmanL-Ligh
/URWBookmanL-LighItal
/URWChanceryL-MediItal
/URWGothicL-Book
/URWGothicL-BookObli
/URWGothicL-Demi
/URWGothicL-DemiObli
/URWPalladioL-Bold
/URWPalladioL-BoldItal
/URWPalladioL-Ital
/URWPalladioL-Roma
/USPSBarCode
/VAGRounded-Black
/VAGRounded-Bold
/VAGRounded-Light
/VAGRounded-Thin
/Verdana
/Verdana-Bold
/Verdana-BoldItalic
/Verdana-Italic
/VerdanaRef
/VinerHandITC
/Viva-BoldExtraExtended
/Vivaldii
/Viva-LightCondensed
/Viva-Regular
/VladimirScript
/Vrinda
/Webdings
/Westminster
/Willow
/Wingdings2
/Wingdings3
/Wingdings-Regular
/WNCYB10
/WNCYI10
/WNCYR10
/WNCYSC10
/WNCYSS10
/WoodtypeOrnaments-One
/WoodtypeOrnaments-Two
/WP-ArabicScriptSihafa
/WP-ArabicSihafa
/WP-BoxDrawing
/WP-CyrillicA
/WP-CyrillicB
/WP-GreekCentury
/WP-GreekCourier
/WP-GreekHelve
/WP-HebrewDavid
/WP-IconicSymbolsA
/WP-IconicSymbolsB
/WP-Japanese
/WP-MathA
/WP-MathB
/WP-MathExtendedA
/WP-MathExtendedB
/WP-MultinationalAHelve
/WP-MultinationalARoman
/WP-MultinationalBCourier
/WP-MultinationalBHelve
/WP-MultinationalBRoman
/WP-MultinationalCourier
/WP-Phonetic
/WPTypographicSymbols
/XYATIP10
/XYBSQL10
/XYBTIP10
/XYCIRC10
/XYCMAT10
/XYCMBT10
/XYDASH10
/XYEUAT10
/XYEUBT10
/ZapfChancery-MediumItalic
/ZapfDingbats
/ZapfHumanist601BT-Bold
/ZapfHumanist601BT-BoldItalic
/ZapfHumanist601BT-Demi
/ZapfHumanist601BT-DemiItalic
/ZapfHumanist601BT-Italic
/ZapfHumanist601BT-Roman
/ZWAdobeF
]
/NeverEmbed [ true
]
/AntiAliasColorImages false
/CropColorImages true
/ColorImageMinResolution 200
/ColorImageMinResolutionPolicy /OK
/DownsampleColorImages true
/ColorImageDownsampleType /Bicubic
/ColorImageResolution 300
/ColorImageDepth -1
/ColorImageMinDownsampleDepth 1
/ColorImageDownsampleThreshold 2.00333
/EncodeColorImages true
/ColorImageFilter /DCTEncode
/AutoFilterColorImages true
/ColorImageAutoFilterStrategy /JPEG
/ColorACSImageDict <<
/QFactor 0.76
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/ColorImageDict <<
/QFactor 1.30
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/JPEG2000ColorACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 10
>>
/JPEG2000ColorImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 10
>>
/AntiAliasGrayImages false
/CropGrayImages true
/GrayImageMinResolution 200
/GrayImageMinResolutionPolicy /OK
/DownsampleGrayImages true
/GrayImageDownsampleType /Bicubic
/GrayImageResolution 300
/GrayImageDepth -1
/GrayImageMinDownsampleDepth 2
/GrayImageDownsampleThreshold 2.00333
/EncodeGrayImages true
/GrayImageFilter /DCTEncode
/AutoFilterGrayImages true
/GrayImageAutoFilterStrategy /JPEG
/GrayACSImageDict <<
/QFactor 0.76
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/GrayImageDict <<
/QFactor 1.30
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/JPEG2000GrayACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 10
>>
/JPEG2000GrayImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 10
>>
/AntiAliasMonoImages false
/CropMonoImages true
/MonoImageMinResolution 400
/MonoImageMinResolutionPolicy /OK
/DownsampleMonoImages true
/MonoImageDownsampleType /Bicubic
/MonoImageResolution 600
/MonoImageDepth -1
/MonoImageDownsampleThreshold 1.00167
/EncodeMonoImages true
/MonoImageFilter /CCITTFaxEncode
/MonoImageDict <<
/K -1
>>
/AllowPSXObjects false
/CheckCompliance [
/None
]
/PDFX1aCheck false
/PDFX3Check false
/PDFXCompliantPDFOnly false
/PDFXNoTrimBoxError true
/PDFXTrimBoxToMediaBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXSetBleedBoxToMediaBox true
/PDFXBleedBoxToTrimBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXOutputIntentProfile (None)
/PDFXOutputConditionIdentifier ()
/PDFXOutputCondition ()
/PDFXRegistryName ()
/PDFXTrapped /False
/CreateJDFFile false
/Description <<
/ARA
/BGR
/CHS
/CHT
/CZE
/DAN
/DEU
/ESP
/ETI
/FRA
/GRE
/HEB
/HRV
/HUN
/ITA
/JPN
/KOR
/LTH
/LVI
/NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor weergave op een beeldscherm, e-mail en internet. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
/NOR
/POL
/PTB
/RUM
/RUS
/SKY
/SLV
/SUO
/SVE
/TUR
/UKR
/ENU (Use these settings to create Adobe PDF documents best suited for on-screen display, e-mail, and the Internet. Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/ConvertColors /ConvertToRGB
/DestinationProfileName (sRGB IEC61966-2.1)
/DestinationProfileSelector /UseName
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /MediumResolution
>>
/FormElements false
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles true
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /NA
/PreserveEditing false
/UntaggedCMYKHandling /UseDocumentProfile
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false
>>
]
>> setdistillerparams
<<
/HWResolution [600 600]
/PageSize [612.000 792.000]
>> setpagedevice