A BIRCH-based clustering method for large time series databases

Abstract

This paper presents a novel approach for time series clustering which is based on BIRCH algorithm. Our BIRCH-based approach performs clustering of time series data with a multi-resolution transform used as feature extraction technique. Our approach hinges on the use of cluster feature (CF) tree that helps to resolve the dilemma associated with the choices of initial centers and significantly improves the execution time and clustering quality. Our BIRCH-based approach not only takes full advantages of BIRCH algorithm in the capacity of handling large databases but also can be viewed as a flexible clustering framework in which we can apply any selected clustering algorithm in Phase 3 of the framework. Experimental results show that our proposed approach performs better than k-Means in terms of clustering quality and running time, and better than I-k-Means in terms of clustering quality with nearly the same running time. © 2012 Springer-Verlag.