Abstract
This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters’ centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for the data, by using the PBM validity index to evaluate the quality of the partition.
The work’s main contributions are the implementation of the entire cluster’s analysis process, which is a new approach in literature, integrating to clusters calculation the finding of the best natural pattern present in data, and also, the parallel processing implementation of this tool, which enables this approach to be used with vary large volumes of data, a increasing need for data analysis in nowadays industries and business databases, making the cluster analysis a feasible tool to support specialist’s decision in all fields of knowledge.
The results presented in the paper show that this approach is scalable and brings processing time reduction as an benefit that parallel processing can bring to the matter of cluster analysis.
Topics of Interest: Unsupervised Classification, Fuzzy c-Means, Cluster and Grid Computing
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Modenesi, M.V., Costa, M.C.A., Evsukoff, A.G., Ebecken, N.F.F. (2007). Parallel Fuzzy c-Means Cluster Analysis. In: Daydé, M., Palma, J.M.L.M., Coutinho, Á.L.G.A., Pacitti, E., Lopes, J.C. (eds) High Performance Computing for Computational Science - VECPAR 2006. VECPAR 2006. Lecture Notes in Computer Science, vol 4395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71351-7_5
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DOI: https://doi.org/10.1007/978-3-540-71351-7_5
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