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A Phenomenal Measurement Used for Congregation of Informations

S. Priyanka, R. Thiyagarajan, T.K.P. Rajagopal


Data mining technology is used for identifying and extracting the patterns from large volume of data. The technology is mainly used for extracting the unknown patterns for real time, financial and business applications. In data mining, the conventional clustering algorithms are used for grouping the data sets. There are number of algorithms which are used to solve this problem. Fuzzy clustering methods have the potential to manage such situations efficiently.  In this paper, we propose a k-means fuzzy clustering method which is more efficient in handling outlier points and has been considered as useful means for identifying patterns and trends of large volume of data. It is a computational intelligence discipline which has emerged as a valuable tool for analyzing the data, discovery of knowledge and decision making.  The  unprocessed  unlabeled  data  from  the  large  volume  of dataset can be categorized initially in an unsupervised fashion by using cluster  analysis i.e. clustering the assignment of a set of remarks into clusters so that remarks in the same cluster may be in some sense be treated as similar. The result of the clustering process and efficiency of its domain application are generally resolute through algorithms.


Clustering, Outlier Points, Knowledge Discovery, K-Means, Time Complexity

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