A novel algorithm for mining closed temporal patterns from interval-based data

Yi Cheng Chen, Julia Tzu Ya Weng, Lin Hui

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Closed sequential patterns have attracted researchers’ attention due to their capability of using compact results to preserve the same expressive power as conventional sequential patterns. However, studies to date have mainly focused on mining conventional patterns from time interval-based data, where each datum persists for a period of time. Few research efforts have elaborated on discovering closed interval-based sequential patterns (also referred to as closed temporal patterns). Mining closed temporal patterns are an arduous problem since the pairwise relationships between two interval-based events are intrinsically complex. In this paper, we develop an efficient algorithm, CCMiner, which stands for Closed Coincidence Miner to discover frequent closed patterns from interval-based data. The algorithm also employs some optimization techniques to effectively reduce the search space. The experimental results on both synthetic and real datasets indicate that CCMiner not only significantly outperforms the prior interval-based mining algorithms in execution time but also possesses graceful scalability. Furthermore, we also apply CCMiner to a real dataset to show the practicability of time interval-based closed pattern mining.

Original languageEnglish
Pages (from-to)151-183
Number of pages33
JournalKnowledge and Information Systems
Volume46
Issue number1
DOIs
StatePublished - 1 Jan 2016

Keywords

  • Closed sequential pattern
  • Closed temporal pattern
  • Coincidence representation
  • Data mining

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