Clustering Algorithm for Time Series Based on Peak Interval

Abstract

Existing clustering algorithms are weak in extracting peak intervals for clustering time series data. In this paper, we propose a new clustering algorithm named Peak Interval Extraction Strategy based Hierarchical Clustering method (PIES_HC) for clustering time series data. The proposed PIES_HC algorithm can effectively exploit inherent peak interval information of a time series data set to enhance clustering performance. The main contributions of our work include the design of a strategy to extract peak intervals of time series data and the development of a new similarity measure method based on peak intervals. With a synthetic data set and three real-life data sets, our experimental results confirm that the proposed PIES_HC algorithm outperforms time series clustering algorithms based on Euclidean Distance and Dynamic Time Warping in terms of Accuracy.