Analysis of Various Clustering Algorithms
Sunila Godara1, Amita Verma2
1Sunila Godara, Assistant Professor, Department of Computer Science Engineering, Guru Jambheshwar University of Science & Technology, Hisar (Haryana), India.
2Ms. Amita Verma, Department of Computer Science Engineering, Guru Jambheshwar University of Science & Technology, Hisar (Haryana), India.
Manuscript received on 11 June 2013 | Revised Manuscript received on 17 June 2013 | Manuscript Published on 30 June 2013 | PP: 186-189 | Volume-3 Issue-1, June 2013 | Retrieval Number: A0948063113/13©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: Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper reviews four types of clustering techniques- k-Means Clustering, Farther first clustering, Density Based Clustering, Filtered clusterer. These clustering techniques are implemented and analyzed using a clustering tool WEKA. Performance of the 4 techniques are presented and compared.
Keywords: Data Clustering, Density Based Clustering, Farther First Clustering, Filtered Clusterer, K-Means Clustering.
Scope of the Article: Clustering