GeoreferencedFrequentPatternMining

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

Bases: PatternMiner

About this algorithm

Description:

This module implements the FSPGrowth algorithm for discovering georeferenced frequent patterns from spatial transactional datasets. In addition to mining frequent itemsets, this algorithm incorporates spatial neighborhood information, allowing it to capture patterns influenced by the proximity of transactions.

This is particularly useful in applications such as geospatial analysis, environmental monitoring, and urban planning where location-aware pattern mining is essential.

Parameters:

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

• nFile (str): Path to the neighborhood file specifying spatial relationships.

Attributes:

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

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

Execution methods

Calling from a Python program

from geoanalytics.patternMining import GeoreferencedFrequentPatternMining

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

miner.run(minSupport=3, nFile="data/neighbor.txt")

Credits

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

run(minSupport: int, nFile: str)

Executes the FSPGrowth algorithm to mine georeferenced frequent patterns using spatial relationships.

Parameters:

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

• nFile (str) – Path to the neighborhood file containing spatial proximity information.

Output:

Prints the georeferenced frequent itemsets to the console.