Analysis and Comparison of Efficient Techniques of Clustering Algorithms in Data Mining
Shiv Pratap Singh Kushwah1, Keshav Rawat2, Pradeep Gupta3
1Shiv Pratap Singh Kushwah, Department of CSE/IT, ITM Universe, Gwalior, India.
2Keshav Rawat, Department of CSE/IT, ITM Universe, Gwalior, India.
3Pradeep Gupta, Department of CSE/IT, GEC, Gwalior, India.
Manuscript received on August 01, 2012. | Revised Manuscript received on August 05, 2012. | Manuscript published on August 10, 2012. | PP: 109-113 | Volume-1 Issue-3, August 2012. | Retrieval Number: C0229071312/2012©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: This paper presents the comparison of data mining algorithms for clustering. These algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.
Keywords: cluster, data mining, clustering method, k-mean.