ANEW APPROACH ON K-MEANS CLUSTERING

To explore the application of feature extraction technique to extract necessary features using k-mean clustering . The main goal of research on feature extraction using k-mean is to find out best features from the cluster analysis. All the implementation can be performed by using Genetic algorithm(G...

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Bibliographic Details
Published in:International journal of computer science and information security 2011-11, Vol.9 (11), p.63
Main Authors: Rout, Trilochan, Mohapatra, Srikanta Kumar, Mohanty, Jayashree, Kamillia, Sushant Ku, Mohapatra, Susant K
Format: Article
Language:English
Subjects:
Algorithms
Classification
Cluster analysis
Customer retention
Data compression
Data mining
Information industry
Methods
Principal components analysis
Studies
Online Access:Get full text
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Summary:To explore the application of feature extraction technique to extract necessary features using k-mean clustering . The main goal of research on feature extraction using k-mean is to find out best features from the cluster analysis. All the implementation can be performed by using Genetic algorithm(GA) also. The same problem is done by using Mat lab. The k-mean clustering process for feature extraction gives accuracy almost equal with that Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).Although this is a unsupervised learning method, before classification of dataset into different class this method can be used to partition the group to obtain the better efficiency with respect to the number of object and attributes this can be developed with same logic and can give better accuracy in Genetic algorithm(GA). [PUBLICATION ABSTRACT]
ISSN:1947-5500