A BIRCH-Based Clustering Method for Large Time Series Databases
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.
Keywordstime series clustering cluster feature DWT BIRCH-based
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- 1.Chan, K., Fu, W.: Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE Intl. Conf. on Data Engineering (ICDE 1999), March 23-26, pp. 126–133 (1999)Google Scholar
- 2.Gavrilov, M., Anguelov, M., Indyk, P., Motwani, R.: Mining The Stock Market: Which Measure is Best? In: Proc. of 6th ACM Conf. on Knowledge Discovery and Data Mining, Boston, MA, August 20-23, pp. 487–496 (2000)Google Scholar
- 4.Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann (2006)Google Scholar
- 5.Kalpakis, K., Gada, D., Puttagunta, V.: Distance Measures for Effective Clustering of ARIMA Time Series. In: Proc. of 2001 IEEE Int. Conf. on Data Mining, pp. 273–280 (2001)Google Scholar
- 6.Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive (2002), http://www.cs.ucr.edu/~eamonn/TSDMA/index.html
- 10.Redmond, S., Heneghan, C.: A Method for Initialization the k-Means Clustering Algorithm Using kd-Trees. Pattern Recognition Letters (2007)Google Scholar
- 14.Historical Data for S&P 500 Stocks, http://kumo.swcp.com/stocks/