Utility Mining Across Multi-Sequences with Individualized Thresholds

TL;DR

This paper introduces USPT, a novel utility mining framework that efficiently discovers high-utility sequential patterns across multiple sequences using individualized thresholds for each item, improving pattern relevance and mining performance.

Contribution

The paper proposes a new utility mining framework USPT with individualized thresholds, utilizing lexicographic-sequential trees and pruning strategies for efficient high-utility pattern discovery.

Findings

USPT effectively finds high-utility sequential patterns with individualized thresholds.

USPT outperforms existing methods in efficiency and effectiveness.

Experimental results confirm USPT's scalability on real-life and synthetic datasets.

Abstract

Utility-oriented pattern mining has become an emerging topic since it can reveal high-utility patterns (e.g., itemsets, rules, sequences) from different types of data, which provides more information than the traditional frequent/confident-based pattern mining models. The utilities of various items are not exactly equal in realistic situations; each item has its own utility or importance. In general, user considers a uniform minimum utility (minutil) threshold to identify the set of high-utility sequential patterns (HUSPs). This is unable to find the interesting patterns while the minutil is set extremely high or low. We first design a new utility mining framework namely USPT for mining high-Utility Sequential Patterns across multi-sequences with individualized Thresholds. Each item in the designed framework has its own specified minimum utility threshold. Based on the lexicographic-sequential tree and the utility-array structure, the USPT framework is presented to efficiently discover the HUSPs. With the upper-bounds on utility, several pruning strategies are developed to prune the unpromising candidates early in the search space. Several experiments are conducted on both real-life and synthetic datasets to show the performance of the designed USPT algorithm, and the results showed that USPT could achieve good effectiveness and efficiency for mining HUSPs with individualized minimum utility thresholds.