A Rough Sets Partitioning Model for Mining Sequential Patterns with Time Constraint

TL;DR

This paper introduces a novel rough sets-based partitioning model for efficient sequential pattern mining with adjustable time constraints, significantly improving speed and providing insights into time intervals of patterns.

Contribution

The paper proposes a new algorithm using rough sets to enhance sequential pattern mining by enabling previsualization and adjustable time constraints, outperforming existing methods.

Findings

Algorithm is at least ten times faster than GSP.

Allows previsualization of patterns before mining.

Determines time intervals of sequential patterns.

Abstract

Now a days, data mining and knowledge discovery methods are applied to a variety of enterprise and engineering disciplines to uncover interesting patterns from databases. The study of Sequential patterns is an important data mining problem due to its wide applications to real world time dependent databases. Sequential patterns are inter-event patterns ordered over a time-period associated with specific objects under study. Analysis and discovery of frequent sequential patterns over a predetermined time-period are interesting data mining results, and can aid in decision support in many enterprise applications. The problem of sequential pattern mining poses computational challenges as a long frequent sequence contains enormous number of frequent subsequences. Also useful results depend on the right choice of event window. In this paper, we have studied the problem of sequential pattern mining through two perspectives, one the computational aspect of the problem and the other is incorporation and adjustability of time constraint. We have used Indiscernibility relation from theory of rough sets to partition the search space of sequential patterns and have proposed a novel algorithm that allows previsualization of patterns and allows adjustment of time constraint prior to execution of mining task. The algorithm Rough Set Partitioning is at least ten times faster than the naive time constraint based sequential pattern mining algorithm GSP. Besides this an additional knowledge of time interval of sequential patterns is also determined with the method.