D Partition Algorithms – A Study and Emergence of Mining Projected Clusters in High - Dimensional Dataset

High-dimensional data has a major challenge due to the inherent sparsity of the points. Existing clustering algorithms are inefficient to the required similarity measure is computed between data points in the full-dimensional space. In this work, a number of projected clustering algorithms have been analyzed. However, most of them encounter difficulties when clusters hide in subspaces with very low dimensionality. These challenges motivate effort to propose a reliable K-mediods partitional distance-based projected clustering algorithm. The proposed process is based on the K-Means and K-Mediods algorithm, with the computation of distance restricted to subsets of attributes where object values are dense. K-mediod algorithm is capable of detecting projected clusters of low dimensionality embedded in a high-dimensional space and avoids the computation of the distance in the full-dimensional space. Our research article is based on analysis of the effective performance of K-mediods.