EFB Grid Based Structure for Discovering Quality Clusters in Density Based Clustering
Clustering is one of the important data mining techniques which discover clusters in many real-world data sets. Recent algorithms attempt to find clusters in subspaces of high dimensional data. Density based clustering algorithms uses grid structure for partitioning each dimensions into intervals (bins) which yields good computation and quality results on large databases. In this paper, the authors propose Equal-Frequency Based (EFB) grid structure for efficient computation of clusters for high dimensional data sets. The computation is reduced by partitioning the bins with equal frequency bin method. The performance evaluation is done with data sets taken from UCI ML Repository. The result gives better quality clusters compared with other grid structures.