Due: 3/22
Note: Show all your work.
Assignment 7
Problem 1 (10 points) This question is about a learning classifier system XCS that we discussed in the class. Consider the following population, which has the current set of rules:
1101 01 1#1# 01 1#0# 01 #0#1 10 #01# 10 10#1 10 1011 01
Suppose that a sample 1011 10 is extracted from the training dataset.
(1). Generate the match set.
(2). Determine the action from the match set.
(3). Generate the action set.
(4). Which rules are rewarded? Which rules are not rewarded?
Problem 2 (20 points). Consider the following transactional database.
TID
Items
100
2, 3, 4, 5, 6, 8
200
1, 2, 3, 5, 6
300
1, 4, 5, 7, 8
400
2, 3, 4, 5, 6
500
1, 2, 3, 4, 5, 7
600
1, 3, 8
(1) Mine all frequent itemsets using the Apriori algorithm we discussed in the class. 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 = 50% (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%.
Itemset
1
2
3
4
5
6
8
Count
4
4
5
4
5
3
3
Problem 3 (20 points) Perform association analysis using JMP Pro following the instructions in JMP-association-analysis-assignment.pdf file.
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.
Submission:
Include all answers in a single file and name it LastName_FirstName_HW7.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_HW7.EXT. Here, ¡°EXT¡± is an appropriate archive file extension (e.g., zip or rar). Upload the file to Blackboard.