An efficient algorithm for mining time interval-based patterns in large database

TLDR

Sequential pattern mining has largely focused on time‑point data, while time‑interval data—common in real applications—presents complex relationships that make large‑database mining challenging. The paper proposes an incision strategy and a coincidence representation to simplify processing of complex event‑interval relations. The authors develop CTMiner, an efficient algorithm that discovers frequent time‑interval patterns and uses two pruning techniques to reduce the search space. Experiments demonstrate that CTMiner is efficient, scalable, and outperforms state‑of‑the‑art algorithms.

Abstract

Most studies on sequential pattern mining are mainly focused on time point-based event data. Few research efforts have elaborated on mining patterns from time interval-based event data. However, in many real applications, event usually persists for an interval of time. Since the relationships among event time intervals are intrinsically complex, mining time interval-based patterns in large database is really a challenging problem. In this paper, a novel approach, named as incision strategy and a new representation, called coincidence representation are proposed to simplify the processing of complex relations among event intervals. Then, an efficient algorithm, CTMiner (Coincidence Temporal Miner) is developed to discover frequent time-interval based patterns. The algorithm also employs two pruning techniques to reduce the search space effectively. Furthermore, experimental results show that CTMiner is not only efficient and scalable but also outperforms state-of-the-art algorithms.