Programming Assignment: Frequent Itemset
Mining Using Apriori
Description
In this programming assignment, you are required to implement the Apriori
algorithm and apply it to mine frequent itemsets from a real-life dataset.
Input
The provided input file (categories.txt) consists of the category lists of 77,185
places in the US. Each line corresponds to the category list of one place, where
the list consists of a number of category instances (e.g., hotels, restaurants, etc.)
that are separated by semicolons.
An example line is provided below:
Local Services;IT Services & Computer Repair
In the example above, the corresponding place has two category instances:
“Local Services” and “IT Services & Computer Repair”.
Output
You need to implement the Apriori algorithm and use it to mine category sets
that are frequent in the input data. When implementing the Apriori algorithm,
you may use any programming language you like. We only need your result
pattern file, not your source code file. After implementing the Apriori algorithm,
please set the relative minimum support to 0.01 and run it on the 77,185 category
lists. In other words, you need to extract all the category sets with absolute
support larger than (non-inclusive) 771.
Part 1
Please output all the length-1 frequent categories with their absolute supports
into a text file named part1.txt. Every line corresponds to exactly one frequent
category and should be in the following format:
1
