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12th International Workshop on Database and Expert Systems Applications (2001)
Munich, Germany
Sept. 3, 2001 to Sept. 7, 2001
ISBN: 0-7695-1230-5
pp: 0133
Elio Masciari , University of Calabria
Giuseppe Raimondo , University of Calabria
Clara Pizzuti , ISI-CNR
Domenico Talia , ISI-CNR
Abstract: Data mining algorithms generally deal with very large data sets that do not fit in main memory. Therefore, techniques that manage huge data sets need to be developed. Any algorithm that is proposed for mining data should have to account for out-of-core data structures. However, most of the existing algorithms haven't yet addressed this issue. In this paper we describe the implementation of an out-of-core technique for the data analysis of very large data sets with the sequential and parallel version of the clustering algorithm AutoClass. We discuss the out-of-core technique and show performance results in terms of execution time and speed up.

G. Raimondo, C. Pizzuti, E. Masciari and D. Talia, "Using an Out-of-Core Technique for Clustering Large Data Sets," 12th International Workshop on Database and Expert Systems Applications(DEXA), Munich, Germany, 2001, pp. 0133.
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