Using an out-of-core technique for clustering large data sets

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 account for out-of-core data structures. However, most of the existing algorithms have not 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 the performance results in terms of execution time and speed up