High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

An Approach to Cluster Data Using Voronoi And Applying SVM for Outlier's.


Maahi A. Talreja , TCET, Mumbai; Sheetal Rathi, TCET, Mumbai


K-Means Clustering, Voronoi Indexing, Data Mining, Support Vector Machine


Data mining an interdisciplinary research area spanning several disciplines such as expert system, database system, statistic, machine learning and intelligent information systems. In last few decades Data mining has becomes a very important and active area of research because of previously unknown and interesting knowledge from very huge data present in day to day life. Different approaches of the data mining are studied and used in several real time applications. When the data sets are analysis different aspects are consider. Clustering is important techniques to form a group of similar or items having similar properties. But the main problem with these clustering of data sets is outliers. Outliers are the unwanted data or similar data in the clusters. In this paper we proposed a system using K-Means clustering algorithm, Voronoi indexing techniques to form a structured cluster and then use the Support Vector Machine to find out the outliers from the clusters.

Other Details

Paper ID: IJSRDV3I80059
Published in: Volume : 3, Issue : 8
Publication Date: 01/11/2015
Page(s): 82-85

Article Preview

Download Article