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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Juho Jokinen, Tomi Räty and Timo Lintonen, "Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1332-1343, Nov. 2019. doi: 10.1109/JAS.2019.1911744
Citation: Juho Jokinen, Tomi Räty and Timo Lintonen, "Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1332-1343, Nov. 2019. doi: 10.1109/JAS.2019.1911744

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

doi: 10.1109/JAS.2019.1911744
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  • 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.

     

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    Highlights

    • Automatically uncovering the existence of a clustering structure from a data set.
    • Assessment of clusterability in a data set.
    • Resolving these issues through a novel density-based clusterability measure.

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