Mining Temporal Patterns in Time Interval-Based Data

TLDR

Sequential pattern mining is a key area of data mining, and recent efforts have focused on time‑interval event data, yet mining interval‑based sequences remains difficult due to the complex relationships between intervals. The study proposes two novel interval representations—endpoint and endtime—and introduces three pruning techniques to simplify the mining of complex interval relationships. Using these representations, the authors define three interval‑based pattern types (temporal, occurrence‑probabilistic, duration‑probabilistic) and develop two algorithms, TPMiner and P‑TPMiner, to discover them. Experiments show that TPMiner and P‑TPMiner efficiently find all three pattern types and that the methods perform effectively on real datasets.

Abstract

Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship between two intervals is intrinsically complex, how to effectively and efficiently mine interval-based sequences is a challenging issue. In this paper, two novel representations, endpoint representation and endtime representation, are proposed to simplify the processing of complex relationships among event intervals. Based on the proposed representations, three types of interval-based patterns: temporal pattern, occurrence-probabilistic temporal pattern, and duration-probabilistic temporal pattern, are defined. In addition, we develop two novel algorithms, Temporal Pattern Miner (TPMiner) and Probabilistic Temporal Pattern Miner (P-TPMiner), to discover three types of interval-based sequential patterns. We also propose three pruning techniques to further reduce the search space of the mining process. Experimental studies show that both algorithms are able to find three types of patterns efficiently. Furthermore, we apply proposed algorithms to real datasets to demonstrate the effectiveness and validate the practicability of proposed patterns.