Review of Existing Clustering Techniques
Amandeep Kaur1, Aanshi Bhardwaj2
1Amandeep Kaur, Department of Computer Science, Lovely Professional University, Phagwara, India.
2Prof. Aanshi Bhardwaj, Department of Computer Science, Lovely Professional University, Phagwara, India.
Manuscript received on May 01, 2016. | Revised Manuscript received on May 04, 2016. | Manuscript published on May 05, 2016. | PP: 50-54 | Volume-6 Issue-2, May 2016. | Retrieval Number: B2845056216
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© The Authors. Published By: 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 mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. In this paper, we representing a review of cluster types, its differential models and algorithms based on this models. Also a new approach is defined here to enhance the functionality of kmeans by introducing the formula of probability distribution for selection of initial seeds.
Keywords: Clustering, Kmeans, Similarity Measures.