Mining temporal patterns in interval-based data

Yi Cheng Chen, Wen-Chih Peng, Suh Yin Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Sequential pattern mining is an important subfield in data mining. Recently, discovering patterns from interval events has attracted considerable efforts due to its widespread applications. However, due to the complex relation between two intervals, mining interval-based sequences efficiently is a challenging issue. In this paper, we develop a novel algorithm, P-TPMiner, to efficiently discover two types of interval-based sequential patterns. Some pruning techniques are proposed to further reduce the search space of the mining process. Experimental studies show that proposed algorithm is efficient and scalable. Furthermore, we apply proposed method to real datasets to demonstrate the practicability of discussed patterns.

Original languageEnglish
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1506-1507
Number of pages2
ISBN (Electronic)9781509020195
DOIs
StatePublished - 22 Jun 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Conference

Conference32nd IEEE International Conference on Data Engineering, ICDE 2016
CountryFinland
CityHelsinki
Period16/05/1620/05/16

Keywords

  • data mining
  • interval-based event
  • representation
  • sequential pattern
  • temporal pattern

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  • Cite this

    Chen, Y. C., Peng, W-C., & Lee, S. Y. (2016). Mining temporal patterns in interval-based data. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 (pp. 1506-1507). [7498397] (2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2016.7498397