CSCI4390-6390 Assign3

Assign3: Pattern Mining

Due Date: Oct 15, before midnight (11:59:59PM)

Closed Itemset Mining

Implement the CHARM closed itemset mining algorithm (Algorithm 9.2 on page 251 in the book). For example, given the dataset:

A B D E
B C E
A B D E
A B C E
A B C D E
B C D

You can assume that each line has a transaction -- a set of items, separated by spaces. For minimum support of 3, the set of closed itemsets should be printed out in the format:

A,B,D,E - 3
A,B,E - 4
B,D - 4
B,C,E - 3
B,C - 4
B,E - 5
B - 6
Number of closed itemsets: 7

Show the frequent closed itemsets on the chess dataset, using minimum support of 2900. The dataset is a set of integer items, one per line, with each line representing a transaction.

Next plot the running time as a function of minimum support from 3000 down to 2500, decreasing the minimum support by 100 each time. Also plot the number of closed frequent itemsets as a function of the same minimum support values.

After testing the tidset approach, you must also implement the diffset version of the code. This means that the subset checks on the tidsets should be replaced by equivalent operations on diffsets. You should create diffsets starting at level 2 (with itemsets of size 2) and not for single items, since the data is very sparse. Show the runtime comparison of the tidset vs. diffset versions for the support values from 3000 down to 2500.

CSCI6390 Only In addition, you must generate all the frequent minimal generators. Show the minimal generators for support 2900, and then plot the number of minimal generators as a function of minimum support from 3000 to 2500.

What to submit

• Submit your notebook named as assign3.ipynb via submitty. If you use AI to assist with the assignment, you must clearly document the tool used, and the prompts used for each part of the assignment. Include the prompts as markdown cells in your notebook (for each part/instance).

Policy on Academic Honesty

You are free to discuss how to tackle the assignment, but all coding must be your own. Any AI tool use must be declared. Any students caught violating the academic honesty principle (e.g., code similarity, or failure to disclose AI tools) will get an automatic F grade on the course and will be referred to the dean of students for disciplinary action.