Clustering structure analysis in time-series data with density-based clusterability measure
Clustering structure analysis in time-series data with density-based clusterability measure
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DOI：
10.1109/jas.2019.1911744
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Juho J. Jokinen;Tomi D. Räty;Timo Lintonen
中科院分区：
文献类型：
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作者：
Juho J. Jokinen;Tomi D. Räty;Timo Lintonen
聚类用于直观地了解数据中的结构。目前的大多数聚类算法即使在不具有这种结构的数据上也会产生一种聚类结构。在这些情况下，算法会强制数据中存在结构，而不是发现结构。为了避免数据关系中的虚假结构，提出了一种新的基于密度的可聚类性度量方法。它衡量数据中聚类结构的显著程度，以评估聚类分析是否能够对数据中的关系产生有意义的洞察。这在时间序列数据中特别有用，因为很难可视化时间序列数据中的结构。在几个合成数据集和时间序列数据集上对该聚类性度量的性能进行了评估，结果表明基于密度的聚类性度量能够很好地刻画时间序列数据的聚类结构。
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.