Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A method for initialising the K-means clustering algorithm using kd-trees
Pattern Recognition Letters
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
ZIB structure prediction pipeline: composing a complex biological workflow through web services
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Comparing three lower bounding methods for DTW in time series classification
Proceedings of the Third Symposium on Information and Communication Technology
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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.