Assignment 8
Due: 4/1
Note: Show all your work.
Problem 1 (20 points). Consider the following transactional database.
(1) Mine all frequent itemsets using Apriori. Show all candidate itemsets and frequent itemsets. You should follow the process described in the book and lecture (i.e., C1 ¡ú L1 ¡ú C2 ¡ú L2 ¡ú …). Minimum support = 60% (or 3 or more transactions). To save your time, L1 is given below:
L1:
(2) Sort all frequent 4-itemsets by their item number. Then, select the first frequent 4-itemset form the sorted list of frequent 4-itemsets and mine all strong rules from this itemset that have the format {W, X} => {Y, Z}, where W, X, Y, and Z are individual items. Assume that minimum confidence = 80%.
Problem 2 (10 points). Consider the following transactional database for sequential pattern mining.
TID
Items
100
1, 2, 3, 4, 5, 7
200
1, 3, 5, 6
300
1, 4, 5, 7, 8
400
1, 2, 3, 4, 5
500
2, 3, 4, 5, 7, 8
Itemset
1
2
3
4
5
7
Count
4
3
4
4
5
3
CID
Day
Items
1
1 14 24 31
B, D, H A, C, D B, D, F E, F, G
2
4
9 14
A, B, G, H C, D, E, G C, D, H
3
1 24 51
B, G, H
A, C, D, E A, D, G, H
4
2 12 25
B, G
A, B, C, H B, C, D, E. G
Determine the supports of the following sequences: <{H}, {B}>, <{A, C}, {E}>, <{C}, {D, G}>
Problem 3 (20 points). Consider the following contingency table.
(1). Compute the lift, all-confidence, cosine, Kulczynski and imbalance ratio measure, and determine whether buying coffee and buying tea are positively correlated, negatively correlated, or not correlated.
(2). Perform the chi-square test with 5% significance level and determine whether they are correlated or not.
Problem 4 (20 points). You will perform association analysis using JMP Pro. There is a section in Predictive and Specialized Modeling.pdf documentation that shows how to perform association analysis. You may want to read this section before starting the assignment. Follow the instructions in JMP-association-analysis-assignment.pdf file.
Submission
Include all answers in a single file and name it lastName_firstName_HW8.EXT. Here, ¡°EXT¡± is an appropriate file extension (e.g., docx or pdf). If you have multiple files, then combine all files into a single archive file. Name the archive file as lastName_firstName_HW8.EXT. Here, ¡°EXT¡± is an appropriate archive file extension (e.g., zip or rar). Upload the file to Blackboard.
C (buys coffee = Yes)
C (buys coffee = No)
T (buys tea = Yes)
473
64
T (buys tea = No)
29
753