Three Controlled-Sized Clustering Methods for Time-Series Data

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  • 時系列データに対する3種類のサイズコントロールクラスタリング

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<p>Time-series data is data that contains information about time-varying phenomena, and it has a wide range of applications. Clustering is one of the data analysis methods to analyze large complex time-series data and extract their features. The important issues in clustering time-series data is the selection of a suitable dissimilarity and the selection of a suitable clustering algorithm. In this paper, we propose new clustering methods to handle imbalanced time-series data by introducing the concept of size-control into the clustering methods for time-series data. The proposed methods are constructed by extending k-medoids using dynamic time warping (DTW) for dissimilarity, k-medoids and k-shape using shape-based distance (SBD) for dissimilarity, which are typical methods for time-series data. The performance of the proposed methods is verified by numerical experiments using 12 datasets available in the UCR Time Series Classification Archive. From the numerical experiments, we confirmed that k-medoids with size control using DTW obtains the best cluster partition among the proposed methods.</p>

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