Fuzzy clustering based segmentation of time-series

Janos Abonyi, Balazs Feil, Sandor Nemeth, Peter Arva

Research output: Contribution to journalArticle

28 Citations (Scopus)


The segmentation of time-series is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a time-series are usually vague and do not focused on any particular time point. Therefore it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to group overlapping and vague objects, they cannot be directly applied to time-series segmentation. This paper proposes a clustering algorithm for the simultaneous identification of fuzzy sets which represent the segments in time and the local PCA models used to measure the homogeneity of the segments. The algorithm is applied to the monitoring of the production of high-density polyethylene.

Original languageEnglish
Pages (from-to)275-285
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - Dec 1 2003


ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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