Unsupervised segmentation of categorical time series into episodes

Paul Cohen, Brent Heeringa, Niall Adams

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

19 Scopus citations

Abstract

This paper describes an unsupervised algorithm for segmenting categorical time series into episodes. The VOTING-EXPERTS algorithm first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over the series and has two "expert methods " decide where in the window boundaries should be drawn. The algorithm successfully segments text into words in four languages. The algorithm also segments time series of robot sensor data into subsequences that represent episodes in the life of the robot. We claim that VOTING-EXPERTS finds meaningful episodes in categorical time series because it exploits two statistical characteristics of meaningful episodes.

Original languageEnglish (US)
Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
Pages99-106
Number of pages8
StatePublished - Dec 1 2002
Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
Duration: Dec 9 2002Dec 12 2002

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other2nd IEEE International Conference on Data Mining, ICDM '02
CountryJapan
CityMaebashi
Period12/9/0212/12/02

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Unsupervised segmentation of categorical time series into episodes'. Together they form a unique fingerprint.

  • Cite this

    Cohen, P., Heeringa, B., & Adams, N. (2002). Unsupervised segmentation of categorical time series into episodes. In Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002 (pp. 99-106). (Proceedings - IEEE International Conference on Data Mining, ICDM).