Targeted Mining of Time-Interval Related Patterns

TL;DR

This paper introduces TaTIRP, a novel algorithm for efficiently mining targeted time-interval-related patterns, addressing computational challenges and improving data analysis in applications like healthcare and finance.

Contribution

The paper presents a new algorithm, TaTIRP, with pruning strategies for targeted TIRP mining, enhancing efficiency and scalability over existing methods.

Findings

TaTIRP outperforms baseline algorithms in accuracy and speed.

Pruning strategies significantly reduce computational overhead.

Effective on both real-world and synthetic datasets.

Abstract

Compared to frequent pattern mining, sequential pattern mining emphasizes the temporal aspect and finds broad applications across various fields. However, numerous studies treat temporal events as single time points, neglecting their durations. Time-interval-related pattern (TIRP) mining is introduced to address this issue and has been applied to healthcare analytics, stock prediction, etc. Typically, mining all patterns is not only computationally challenging for accurate forecasting but also resource-intensive in terms of time and memory. Targeting the extraction of time-interval-related patterns based on specific criteria can improve data analysis efficiency and better align with customer preferences. Therefore, this paper proposes a novel algorithm called TaTIRP to discover Targeted Time-Interval Related Patterns. Additionally, we develop multiple pruning strategies to eliminate redundant extension operations, thereby enhancing performance on large-scale datasets. Finally, we conduct experiments on various real-world and synthetic datasets to validate the accuracy and efficiency of the proposed algorithm.