Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events

Shin Yi Wu, Yen Liang Chen

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Previous sequential pattern mining studies have dealt with either point-based event sequences or interval-based event sequences. In some applications, however, event sequences may contain both point-based and interval-based events. These sequences are called hybrid event sequences. Since the relationships among both kinds of events are more diversiform, the information obtained by discovering patterns from these events is more informative. In this study we introduce a hybrid temporal pattern mining problem and develop an algorithm to discover hybrid temporal patterns from hybrid event sequences. We carry out an experiment using both synthetic and real stock price data to compare our algorithm with the traditional algorithms designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of our algorithm is satisfactory. In addition, the experiment also shows that the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns.

Original languageEnglish
Pages (from-to)1309-1330
Number of pages22
JournalData and Knowledge Engineering
Volume68
Issue number11
DOIs
StatePublished - Nov 2009

Keywords

  • Data mining
  • Hybrid event sequences
  • Hybrid temporal pattern
  • Sequential pattern
  • Temporal pattern

Fingerprint

Dive into the research topics of 'Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events'. Together they form a unique fingerprint.

Cite this