FaultTolerantFrequentPatternMining

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

Bases: PatternMiner

About this algorithm

Description:

This module implements the FTFPGrowth algorithm for discovering fault-tolerant frequent patterns in transactional datasets. Fault-tolerant patterns allow a limited number of item mismatches while still preserving a minimum support threshold, making the algorithm useful in noisy or imperfect data environments.

Parameters:

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

Attributes:

  • inputFile (str): Input file provided during object instantiation.

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

Execution methods

Calling from a Python program

from geoanalytics.patternMining import FaultTolerantFrequentPatternMining

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

miner.run(minSupport=3, itemSup=2, minLength=2, faultTolerance=1)

Credits

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

run(minSupport: int, itemSup: int, minLength: int, faultTolerance: int)[source]

Executes the FTFPGrowth algorithm to mine fault-tolerant frequent patterns.

Parameters:

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

  • itemSup (int) – Minimum individual item frequency threshold.

  • minLength (int) – Minimum length of the itemset to be considered.

  • faultTolerance (int) – Maximum number of tolerated faults (missing or mismatched items) in the itemsets.

Output:

Prints the fault-tolerant frequent patterns to the console.