Issue No.03 - May/June (2003 vol.15)
<p><b>Abstract</b>—Data clustering is an important task in the area of data mining. Clustering is the unsupervised classification of data items into homogeneous groups called <it>clusters</it>. Clustering methods partition a set of data items into clusters, such that items in the same cluster are more similar to each other than items in different clusters according to some defined criteria. Clustering algorithms are computationally intensive, particularly when they are used to analyze large amounts of data. A possible approach to reduce the processing time is based on the implementation of clustering algorithms on scalable parallel computers. This paper describes the design and implementation of <it>P-AutoClass</it>, a parallel version of the <it>AutoClass</it> system based upon the Bayesian model for determining optimal classes in large data sets. The <it>P-AutoClass</it> implementation divides the clustering task among the processors of a multicomputer so that each processor works on its own partition and exchanges intermediate results with the other processors. The system architecture, its implementation, and experimental performance results on different processor numbers and data sets are presented and compared with theoretical performance. In particular, experimental and predicted scalability and efficiency of <it>P-AutoClass</it> versus the sequential <it>AutoClass</it> system are evaluated and compared.</p>
Data mining, parallel processing, knowledge discovery, data clustering, unsupervised classification, isoefficiency, scalability.
Clara Pizzuti, "P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets", IEEE Transactions on Knowledge & Data Engineering, vol.15, no. 3, pp. 629-641, May/June 2003, doi:10.1109/TKDE.2003.1198395