Abstract
Cluster analysis—as a technique for grouping a set of objects into similar clusters—is an integral part of data analysis and has received wide interest among data mining specialists. The parallel wavelet-based clustering algorithm using discrete wavelet transforms has been shown to extract the approximation component of the input data on which objects of the clusters are detected based on the object connectivity property. However, this algorithm suffers from inefficient I/O operations and performance degradation due to redundant data processing. We address these issues to improve the parallel algorithm’s efficiency and extend the algorithm further by investigating two merging techniques (both merge-table and priority-queue based approaches), and apply them on three-dimensional data. In this study, we compare two parallel WaveCluster algorithms and a parallel K-means algorithm to evaluate the implemented algorithms’ effectiveness.
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Acknowledgments
Compute, storage and other resources from the Division of Research Computing in the Office of Research and Graduate Studies at Utah State University are gratefully acknowledged. We also would like to thank Dr. Wei-keng Liao for providing us with the source code of the parallel K-means algorithm.
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Yıldırım, A.A., Watson, D. A comparative study of the parallel wavelet-based clustering algorithm on three-dimensional dataset. J Supercomput 71, 2365–2380 (2015). https://doi.org/10.1007/s11227-015-1385-0
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DOI: https://doi.org/10.1007/s11227-015-1385-0