Data mining using hierarchical virtual k-means approach integrating data fragments in cloud computing environment

Abstract

State of the art research in data mining is focusing on loosely distributed regionalized large scale databases using cloud computing for business applications. Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Realization of maximum efficiency depends much on the initiation of accurate decision data mining. This paper presents a specific method of implementing k-means approach for data mining in such scenarios. In this approach data is geographically distributed in multiple regions formed under several virtual machines. The results show that hierarchical virtual k-means approach is an efficient mining scheme for cloud databases.