GeoreferencedPartialPeriodicPatternMining

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

Bases: PatternMiner

About this algorithm

Description:

This module implements the STEclat algorithm to mine georeferenced partial periodic patterns from temporal transactional datasets. This algorithm finds itemsets that appear periodically within certain time constraints, incorporating spatial neighborhood information via a neighborhood file.

This approach is valuable in spatiotemporal data mining applications such as environmental data analysis, urban monitoring, and other domains requiring discovery of partial periodic patterns with spatial context.

Parameters:

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

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

Attributes:

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

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

Execution methods

Calling from a Python program

from geoanalytics.patternMining import GeoreferencedPartialPeriodicPatternMining

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

miner.run(minPS=3, maxIAT=5, nFile="data/neighbor.txt")

Credits

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

run(minPS: int, maxIAT: int, nFile: str)

Executes the STEclat algorithm to mine georeferenced partial periodic patterns.

Parameters:

• minPS (int) – Minimum partial periodicity support threshold.

• maxIAT (int) – Maximum inter-arrival time allowed between pattern occurrences.

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

Output:

Prints the discovered georeferenced partial periodic patterns to the console.