Microsoft Word – Ex_20190112
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The Chinese University of Hong Kong
Dept of Decision Sciences & Managerial Economics
DSME6650: Individual Exercise
Date Due: 10 Feb 2019 (23:55)
Submission: The exercise is in individual basis. Every student should submit his/her own
assignment. You should follow the University guidelines as mentioned in
http://www.cuhk.edu.hk/policy/academichonesty/ to have an electronic
submission. Please submit it to (i) VeriGuide and (ii) the blackboard. Please
contact your tutor if you have any problem during your submission.
Question 1
(a) 15 data points are arranged and settled into three clusters as follows:
Cluster 1 Cluster 2 Cluster 3
Centroid at
(15.5, 28.5)
Centroid at
(43.75, 19.75)
Centroid at
(10.4, 10.6)
(11, 34) (42, 25) (5, 12)
(16, 31) (47, 21) (10, 18)
(18, 24) (45, 13) (15, 8)
(20, 23) (41, 20) (9, 4)
(10, 27) (13, 11)
(18, 32)
(i) Which is the nearest cluster of Cluster 1?
(ii) What are the average Silhouette coefficients of the three clusters?
(iii) What is the average Silhouette coefficient of the entire dataset?
(iv) Which cluster is more cohesive than others?
(v) Justify whether the choice k=3 for the clustering is appropriate or not.
(b) Apply the fuzzy c-means technique on the dataset with three initial centroids, (15, 28),
(40, 20) and (10, 8) for three iterations. Compare your result in (a).
Question 2
Consider the training instances shown in Table Q2 for a dichotomous decision problem.
(a) What is the entropy of this collection of customer instances with respect to the target
attribute with value “Stay”?
(b) What are the information gains of the two attributes x1 and x2 relative to the customer
instances?
(c) For the attribute x3, identify the best threshold by computing the information gain for
every possible split.
(d) What is the best split, among x1, x2, and x3, according to the information gain?
(e) What is the best split, between x1 and x2, according to the Gini index?
(f) Devise two different but complete decision trees for the instances shown in the table using
information gain.
CID x1 x2 x3 Target attribute
01 M HIGH 1.0 Stay
02 M HIGH 6.0 Stay
03 M LOW 5.0 Churn
04 F LOW 4.0 Stay
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05 F HIGH 7.0 Churn
06 F HIGH 3.0 Churn
07 F LOW 8.0 Churn
08 M LOW 7.0 Stay
09 F HIGH 5.0 Churn
Table Q2: Instances show whether the customers stay and churn in a brand
Question 3
Risk assessment is important for financial institution, especially in loan applications. Some
have already implemented their own credit-scoring mechanisms to evaluate their clients’ risk
and make decisions based on this indicator. In fact, the data gathered by financial institutions
is valuable source of information to create information assets, from which credit-scoring
mechanisms can be developed. The purpose of this question is to, from information assets,
create a decision mechanism that is able to evaluate a client’s risk. The attached data files
contain 700 training instances and 300 test instances. The training instances are used to train
decision models while the test instances are used for the evaluation. The data set itself is a
combination of personal, social and financial information about past bank clients. The
complete list of attributes is shown in Table Q3.
No. Attribute Description
1 Status Status of checking account
2 Duration Loan duration in months
3 Credit history Client’s credit history
4 Purpose Purpose of the loan
5 Credit amount Loan amount
6 Savings Saving accounts or bonds
7 Employment duration Duration of the present employment
8 Instalment rate Instalment rate of disposable income
9 Personal status Personal marital status and sex
10 Debtors Other debtors/guarantors
11 Residence Present residence since
12 Property Property owned
13 Age Age in years
14 Instalment plans Other instalment plans
15 Housing House status
16 Existing credits Number of existing credits at the bank
17 Job Job category
18 Liable people Number of people liable to
19 Telephone Existence of telephone number
20 Foreign worker Native or foreign worker
21 Classification Credit classification
Table Q3: Description of attributes
(a) Explain what are the pre-processing procedure(s), if any, that should be taken before the
data mining.
(b) Without any data preprocessing, compare the training and test results on the following
major classification algorithms using Weka. You have to produce a short report, with not
more than 800 words, to explain your findings.
(i) Logistic regression
(ii) Ridge logistic regression for the optimal ridge
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(iii) k-NN, for the optimal value of k.
(iv) Decision Tree, using J48
(v) Random Forest
Question 4
Study the business case “Customer Segmentation” carefully.
(a) Produce a short summary, not more than 300 words, explain how the customer
segmentation can be achieved.
(b) Using not more than 500 words, list out all the major steps in the KDD approach. In each
step, describe clearly, in form of a table,
(i) what is/are the objective(s) of the step.
(ii) what is/are the major technique(s) or data mining algorithm(s) involved.
(iii) what is/are the precaution(s) that should be taken when the techniques or algorithms
are applied.
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