FrequentPatternMining

class geoanalytics.patternMining.FrequentPatternMining.FrequentPatternMining(inputFile: str)

Bases: PatternMiner

About this algorithm

Description:

This module implements the FPGrowth algorithm for mining frequent itemsets from transactional datasets. Frequent itemset mining is a foundational data mining technique used to identify patterns that occur frequently together in a database, and is widely used in market basket analysis, web usage mining, and bioinformatics.

Parameters:

• inputFile (str): Path to the input transactional database file.

Attributes:

• inputFile (str): The transactional input file provided during object initialization.

• miner (FPGrowth): Instance of the FPGrowth algorithm from the PAMI library.

Execution methods

Calling from a Python program

from geoanalytics.patternMining import FrequentPatternMining

miner = FrequentPatternMining("data/input.txt")

miner.run(minSupport=3)

Credits

Written by M. Charan Teja, under the guidance of Professor Rage Uday Kiran.

run(minSupport: int)

Runs the FPGrowth algorithm to mine frequent itemsets.

Parameters:

minSupport (int) – Minimum support threshold for identifying frequent patterns.

Output:

Prints the frequent itemsets to the console.