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CLUSTERING UNCERTAIN DATA BASED ON FUZZY C-MEANS CLUSTERING

Author(s):

SOBIN MATHEW , Maharaja Prithvi Engineering College Avinashi

Keywords:

Fuzzy Logic, Clustering, Soft Computing.

Abstract

Data clusteringis the process of dividing data elements into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. Depending on the nature of the data and the purpose for which clustering is being used, different measures of similarity may be used to place items into classes, where the similarity measure controls how the clusters are formed.In many applications, data contains inherent uncertainty. A number of factors contribute to the uncertainty such as the random nature of the physical data generation and collection process and measurement error. As an example, consider the problem of clustering mobile devices continuously according to the periodic updates of their locations. One application of the clustering is the selection of a device as the leader for each cluster.Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method developed by is frequently used in pattern recognition. FCM provides more value for validity index.Using FCM, more accurate clustering can be performed.

Other Details

Paper ID: IJSRDV2I4053
Published in: Volume : 2, Issue : 4
Publication Date: 01/07/2014
Page(s): 90-93

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