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The Scientific World Journal
Volume 2014, Article ID 562194, 12 pages
Research Article

A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

Faculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, Malaysia

Received 4 October 2013; Accepted 2 February 2014; Published 25 March 2014

Academic Editors: H. Chen, P. Ji, and Y. Zeng

Copyright © 2014 Saeed Aghabozorgi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.