Clustering structure analysis in time-series data with density-based clusterability measure

Juho Jokinen, Tomi Raty, Timo Lintonen

    Research output: Contribution to journalArticle

    Abstract

    Clustering is used to gain an intuition of the structures in the data. Most of the current clustering algorithms produce a clustering structure even on data that do not possess such structure. In these cases, the algorithms force a structure in the data instead of discovering one. To avoid false structures in the relations of data, a novel clusterability assessment method called density-based clusterability measure is proposed in this paper. It measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningful insight to the relationships in the data. This is especially useful in time-series data since visualizing the structure in time-series data is hard. The performance of the clusterability measure is evaluated against several synthetic data sets and time-series data sets, which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
    Original languageEnglish
    Article number8894746
    Pages (from-to)1332-1343
    Number of pages12
    JournalIEEE/CAA Journal of Automatica Sinica
    Volume6
    Issue number6
    DOIs
    Publication statusPublished - Nov 2019
    MoE publication typeA1 Journal article-refereed

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