PartialPeriodicFrequentPatternMining

class geoanalytics.patternMining.PartialPeriodicFrequentPatternMining.PartialPeriodicFrequentPatternMining(inputFile: str)[source]

Bases: PatternMiner

About this algorithm

Description:

This module implements the GPFgrowth algorithm for mining partial periodic frequent patterns from temporal transactional databases. The algorithm identifies frequent itemsets that exhibit periodic behavior with partial periodicity constraints, controlled by maximum periodicity and minimum periodic repetition parameters.

Parameters:

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

Attributes:

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

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

Execution methods

Calling from a Python program

from geoanalytics.patternMining import PartialPeriodicFrequentPatternMining

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

miner.run(minSupport=3, maxPer=10, minPR=2)

Credits

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

run(minSupport: int, maxPer: int, minPR: int)[source]

Executes the GPFgrowth algorithm to mine partial periodic frequent patterns.

Parameters:

  • minSupport (int) – Minimum support threshold for frequent itemsets.

  • maxPer (int) – Maximum periodicity threshold controlling pattern recurrence interval.

  • minPR (int) – Minimum periodic repetition threshold specifying minimum occurrences in periodic cycles.

Output:

Prints the discovered partial periodic frequent patterns to the console.