Secure Clustering in a Distributed Network
S. Harippriya1, T. Kalaikumaran2, S. Karthik3
1S. Harippriya, ME-Software Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India.
2Dr. T. Kalaikumaran, HOD/CSE, SNS College of Technology, Coimbatore, Tamil Nadu. India.
3Dr. S. Karthik, DEAN/CSE, SNS College of Technology, Coimbatore, Tamil Nadu. India.
Manuscript received on April 14, 2013. | Revised Manuscript Received on April 11, 2013. | Manuscript published on April 20, 2013. | PP: 9-13 | Volume-1, Issue-5, April 2013. | Retrieval Number: E0200041513/2013©BEIESP
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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 plays a major role in storage of vast quantities of data. It extracts valuable knowledge, which helps organizations to obtain better results by pooling their data together. Distributed data mining is concerned about data that are shared among multiple organizations. A complementary approach to privacy-preserving data mining uses randomization techniques. Privacy-preserving data mining solutions have been presented both with respect to horizontally and vertically portioned databases, in which earlier data objects with the same attributes for the same data objects are owned by each party, respectively. The quality of a set of clusters can be measured using the value of an objective function which is taken to be the sum of the squares of the distances of each point from the centre of the cluster to which it is assigned.
Keywords: Arbitrarily partitioned Data, Data Mining.