Mining nonambiguous temporal patterns for interval-based events

Shin Yi Wu, Yen Liang Chen

Research output: Contribution to journalArticlepeer-review

133 Scopus citations

Abstract

Previous research on mining sequential patterns mainly focused on discovering patterns from point-based event data. Little effort has been put toward mining patterns from interval-based event data, where a pair of time values is associated with each event. Kam and Fu's work [31] in 2000 identified 13 temporal relationships between two intervals. According to these temporal relationships, a new variant of temporal patterns was defined for interval-based event data. Unfortunately, the patterns defined in this manner are ambiguous, which means that the temporal relationships among events cannot be correctly represented in temporal patterns. To resolve this problem, we first define a new kind of nonambiguous temporal pattern for interval-based event data. Then, the TPrefixSpan algorithm is developed to mine the new temporal patterns from interval-based events. The completeness and accuracy of the results are also proven. The experimental results show that the efficiency and scalability of the TPrefixSpan algorithm are satisfactory. Furthermore, to show the applicability and effectiveness of temporal pattern mining, we execute experiments to discover temporal patterns from historical Nasdaq data.

Original languageEnglish
Pages (from-to)742-758
Number of pages17
JournalIEEE Transactions on Knowledge and Data Engineering
Volume19
Issue number6
DOIs
StatePublished - Jun 2007

Keywords

  • Data mining
  • Interval-based events
  • Sequential patterns
  • Temporal pattern

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