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

Juho Jokinen, Tomi Raty, Timo Lintonen

Research output: Contribution to journalArticleScientificpeer-review

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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Time series
Cluster analysis
Clustering algorithms

Cite this

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title = "Clustering structure analysis in time-series data with density-based clusterability measure",
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.",
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Clustering structure analysis in time-series data with density-based clusterability measure. / Jokinen, Juho; Raty, Tomi; Lintonen, Timo.

In: IEEE/CAA Journal of Automatica Sinica, Vol. 6, No. 6, 8894746, 11.2019, p. 1332-1343.

Research output: Contribution to journalArticleScientificpeer-review

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