Controlled-Sized Clustering for Time-Series Data

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.