Shape-Based clustering for time series data

Authors:
Warissara Meesrikamolkul;Vit Niennattrakul;Chotirat Ann Ratanamahatana
Affiliations:
Department of Computer Engineering, Chulalongkorn University, Pathumwan, Bangkok, Thailand;Department of Computer Engineering, Chulalongkorn University, Pathumwan, Bangkok, Thailand;Department of Computer Engineering, Chulalongkorn University, Pathumwan, Bangkok, Thailand
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2012

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Abstract

One of the most famous algorithms for time series data clustering is k -means clustering with Euclidean distance as a similarity measure. However, many recent works have shown that Dynamic Time Warping (DTW) distance measure is more suitable for most time series data mining tasks due to its much improved alignment based on shape. Unfortunately, k -means clustering with DTW distance is still not practical since the current averaging functions fail to preserve characteristics of time series data within the cluster. Recently, Shape-based Template Matching Framework (STMF) has been proposed to discover a cluster representative of time series data. However, STMF is very computationally expensive. In this paper, we propose a Shape-based Clustering for Time Series (SCTS) using a novel averaging method called Ranking Shape-based Template Matching Framework (RSTMF), which can average a group of time series effectively but take as much as 400 times less computational time than that of STMF. In addition, our method outperforms other well-known clustering techniques in terms of accuracy and criterion based on known ground truth.