Controlled-Sized Clustering for Time-Series Data
Controlled-Sized Clustering for Time-Series Data
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DOI：
10.1109/scisisis50064.2020.9322749
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Nobuhiko Tsuda;Y. Hamasuna
中科院分区：
文献类型：
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作者：
Nobuhiko Tsuda;Y. Hamasuna
时间序列数据的分析在生物学、经济学等各个领域都得到了积极的研究。聚类是一种基于相似性度量将一组对象总结为几个对象子集的方法。有必要定义对象之间的适当相似性。在处理时间序列数据时，还需要考虑几种不变性，包括平移不变性。$k$形聚类是一种具有代表性的时间序列数据聚类方法。众所周知，$k$形状聚类是一种考虑时间序列数据的多种不变性的算法。在$k$形状聚类中使用的相异度对时间序列数据特征的差异具有健壮性。本文提出了一种可控大小的$k$形聚类来处理不平衡数据。数值实验表明，与$k$形聚类相比，该方法没有表现出明显的性能。
The analysis of time-series data has been actively studied in various fields, such as biology and economics. Clustering is a method that summarizes a set of objects into several subsets of objects based on similarity measures. It is necessary to define a suitable similarity between objects. When dealing with time-series data, it is also necessary to consider several invariances, including shift-invariance. $k$-Shape clustering is one of the representative clustering methods for time-series data. It is known that the $k$-Shape clustering is an algorithm, which considers several invariances of time-series data. The dissimilarity used in $k$-Shape clustering is robust to differences in time series data features. In this paper, the controlled-sized $k$-Shape clustering is proposed to handle imbalanced data. Numerical experiments suggest that the proposed method does not show outstanding performance compared to $k$-Shape clustering.