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Author(s):

Aishwarya Kulkarni , AISSMS IOIT; S.N.Zaware, AISSMS IOIT; Arti Ghodekar, AISSMS IOIT; Sonali Tate, AISSMS IOIT

Keywords:

accuracy, categorization, clustering, data mining, improved k-means, time complexity

Abstract:

An increase number of services are emerging on internet due to Service Computing. As a result, service-relevant data become too big for effectively processing by traditional approaches. In the view of this challenge we adopt a Clustering based approach in order to group similar services in same clusters. Clustering groups objects based on the information found in data describing the object and their relationship. We focus on that the objects in the group which would be similar to one another and different from object of the groups. The greater the similarity within a group and greater the difference between groups, better will be the clustering. Our approach will move towards finding the nearest neighbor through Improved K-Means. Our system will provide a structured categorization of dataset for effective searching relevant data through given clusters. The scattered data in form of links with more of irrelevant to the topic was main issue. In order to come over this issue our approach aims to group similar services in same clusters for efficiently harvesting search query results.


Other Details:

Manuscript Id :IJSTEV2I11117
Published in :Volume : 2, Issue : 11
Publication Date: 01/06/2016
Page(s): 448-451
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