Mining Target-Oriented Sequential Patterns with Time-Intervals

TL;DR

This paper introduces an algorithm for mining target-oriented sequential patterns with time-intervals by reversing sequences, filtering irrelevant data, and applying clustering analysis to extract meaningful patterns.

Contribution

It presents a novel approach combining sequence reversal and clustering to efficiently discover time-interval target-oriented sequential patterns.

Findings

Effective extraction of target-oriented patterns with time-intervals

Improved accuracy in identifying relevant sequential patterns

Demonstrated algorithm's efficiency on real datasets

Abstract

A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets. In this paper we present an algorithm to discover target-oriented sequential pattern with time-intervals. To this end, the original sequences are reversed so that the last itemsets can be arranged in front of the sequences. The contrasts between reversed sequences and the concerned itemset are then used to exclude the irrelevant sequences. Clustering analysis is used with typical sequential pattern mining algorithm to extract the sequential patterns with time-intervals between successive itemsets. Finally, the discovered time-interval sequential patterns are reversed again to the original order for searching the target patterns.