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Comparative Analysis of Various Clustering Algorithms based on Green Computing Perspective
One of the fundamental difficulties that arise in several fields, comprising pattern recognition, machine learning and statistics, is clustering. The concept of green computing has attracted much attention recently in cluster computing. However, previous local approaches focused on saving the energy cost of the components in a single workstation without a global vision on the whole cluster, so it achieved undesirable power reduction effect. Other cluster-wide energy saving techniques could only be applied to homogeneous workstations and specific applications. The concept of green computing is a novel approach that uses live migration of virtual machines to transfer load among the nodes on a multilayer ring-based overlay. This scheme can reduce the power consumption greatly by regarding all the cluster nodes as a whole. Also that it can be applied to both the homogeneous and heterogeneous servers. Experimental measurements show that the new method can reduce the power consumption by 74.8% over base at most with certain adjustably acceptable overhead. The basic data clustering problem might be defined as finding out groups in data or grouping related objects together. A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them. The major objective of clustering is to discover collection of comparable objects based on similarity metric. On the other hand, a similarity metric is generally specified by the user according to his requirements for obtaining better results. So far, there is no such technique available which absolutely fits for all applications. Some of the major difficulties concerning the existing available clustering approaches are that they do not concentrate on the entire needs effectively and require huge time complexity in case of clustering a great number of dimensions and bulky data sets. Efficiency of a particular clustering approach chiefly based on the definition of the distance, means that the measure of distance between the two objects in a particular cluster should be well defined using effective distance measures. Also it is necessary to know about the effect of constraints in clustering the objects. The use of constraints in clustering along with the effective distance measures will definitely provide better clustering results. So in order to provide better clustering approaches that fits for all applications and to improve the efficiency of data clustering, this paper proposes a comparative analysis of various algorithms to show the efficient data clustering algorithm.
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