CorrelatedPatternMining

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

Bases: PatternMiner

About this algorithm

Description:

This module implements the CoMinePlus algorithm for mining correlated patterns from a transactional dataset. Correlated pattern mining identifies itemsets that not only occur frequently together but also exhibit strong correlations based on statistical measures like All-Confidence.

Parameters:

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

Attributes:

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

  • miner (CoMinePlus): Object of CoMinePlus algorithm from the PAMI library.

Execution methods

Calling from a Python program

from geoanalytics.patternMining import CorrelatedPatternMining

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

miner.run(minSupport=3, minAllConf=0.6)

Credits

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

run(minSupport: int, minAllConf: int | float)[source]

Runs the CoMinePlus algorithm for correlated pattern mining.

Parameters:

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

  • minAllConf (float or int) – Minimum all-confidence threshold for identifying correlation.

Output:

Prints correlated itemsets to the console.