Modified Dynamic Algorithm of Data Clustering Using Fuzzy C Mean Algorithm
Priyanka Sharma1, Anu Aggarwal2
1Ms. Priyanka Sharma, Department of Computer Science and Engg., Kurukshetra University, Doon Valley Institute of Engg And Technology, Karnal (Haryana), India.
2Ms. Anu Aggarwal, Lecturer, Department of Computer Science and Engg., Kurukshetra University, Doon Valley Institute of Engg And Technology, Karnal (Haryana), India.
Manuscript received on 18 August 2012 | Revised Manuscript received on 25 August 2012 | Manuscript published on 30 August 2012 | PP: 100-103 | Volume-1 Issue-3, August 2012 | Retrieval Number: C0276071312/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering is a division of data into group of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar compared to objects of other group. Dynamic-means is a widely used clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation is needed to reveal whether the optimal number of clusters can be found. This paper presents a modified Dynamic-means algorithm with the intension of improving cluster quality. In dynamic mean algorithm each data elements can be a member of one and only one cluster at a time. The proposed works apply Fuzzy c means algorithm over dynamic-means algorithm to improve the membership grade i.e. each data element can show their membership in each and every clusters.
Keywords: Clustering, Dynamic Mean Clustering And Fuzzy C Mean Clustering.
Scope of the Article: Fuzzy Logics