High Utility Interval-Based Sequences

TL;DR

This paper introduces a novel framework and algorithm for mining high utility patterns in interval-based sequences, addressing the limitations of traditional point-based and utility-agnostic methods, with demonstrated effectiveness on real datasets.

Contribution

It proposes a new utility-aware interval sequence framework and the HUIPMiner algorithm, incorporating pruning for efficient high utility pattern mining.

Findings

HUIPMiner effectively mines high utility interval sequences.

The framework considers overlapping and varying-length events.

Experimental results validate the algorithm's efficiency.

Abstract

Sequential pattern mining is an interesting research area with broad range of applications. Most prior research on sequential pattern mining has considered point-based data where events occur instantaneously. However, in many application domains, events persist over intervals of time of varying lengths. Furthermore, traditional frameworks for sequential pattern mining assume all events have the same weight or utility. This simplifying assumption neglects the opportunity to find informative patterns in terms of utilities, such as cost. To address these issues, we incorporate the concept of utility into interval-based sequences and define a framework to mine high utility patterns in interval-based sequences i.e., patterns whose utility meets or exceeds a minimum threshold. In the proposed framework, the utility of events is considered while assuming multiple events can occur coincidentally and persist over varying periods of time. An algorithm named High Utility Interval-based Pattern Miner (HUIPMiner) is proposed and applied to real datasets. To achieve an efficient solution, HUIPMiner is augmented with a pruning strategy. Experimental results show that HUIPMiner is an effective solution to the problem of mining high utility interval-based sequences.