代写database、Java语言代做、代写c/c++，Python程序设计

日期：2020-04-04 10:21

Assignment 8

Due: 4/1

Note: Show all your work.

Problem 1 (20 points). Consider the following transactional database.

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

(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 → L1 → C2 → L2

→ …). Minimum support = 60% (or 3 or more transactions). To save your time, L1 is given

below:

L1:

Itemset 1 2 3 4 5 7

Count 4 3 4 4 5 3

(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.

Determine the supports of the following sequences:

<{H}, {B}>, <{A, C}, {E}>, <{C}, {D, G}>

Problem 3 (20 points). Consider the following contingency table.

C (buys coffee = Yes) C (buys coffee = No)

T (buys tea = Yes) 473 64

T (buys tea = No) 29 753

(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.