Accelerating k-medoid-based algorithms through metric access methods

摘要

Scalable data mining algorithms have become crucial to efficiently support KDD processes on large databases. In this paper, we address the task of scaling up k-medoid-based algorithms through the utilization of metric access methods, allowing clustering algorithms to be executed by database management systems in a fraction of the time usually required by the traditional approaches. We also present an optimization strategy that can be applied as an additional step of the proposed algorithm in order to achieve better clustering solutions. Experimental results based on several datasets, including synthetic and real ones, show that the proposed algorithm can reduce the number of distance calculations by a factor of more than three thousand times when compared to existing algorithms, while producing clusters of equivalent quality.