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Data Classification by using Homogeneous Clustering Process

by Bhavana R. Mundane, Swati V. Shinde
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 5
Year of Publication: 2016
Authors: Bhavana R. Mundane, Swati V. Shinde
10.5120/ijca2016910691

Bhavana R. Mundane, Swati V. Shinde . Data Classification by using Homogeneous Clustering Process. International Journal of Computer Applications. 146, 5 ( Jul 2016), 6-10. DOI=10.5120/ijca2016910691

@article{ 10.5120/ijca2016910691,
author = { Bhavana R. Mundane, Swati V. Shinde },
title = { Data Classification by using Homogeneous Clustering Process },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 5 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number5/25392-2016910691/ },
doi = { 10.5120/ijca2016910691 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:31.042736+05:30
%A Bhavana R. Mundane
%A Swati V. Shinde
%T Data Classification by using Homogeneous Clustering Process
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 5
%P 6-10
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we have performed dynamic clustering based on classification. The Enhance Neuro-fuzzy system for classification using dynamic clustering presented in this paper is an extension of the original Neuro-fuzzy method for linguistic feature selection and rule-based classification. In the dynamic clustering process, the parameter values like centroid, threshold value and standard deviation are estimated. This parameter is used for creating the cluster. The Gaussian membership function is applied to these clusters to generate the binary value of each feature to given cluster. Using this method we have got the large number of cluster and minimum accuracy. To reduce the cluster size and to improve the accuracy we have implement the homogenous clustering process. Using this process we have minimize the cluster size, and also improve the accuracy.

References
  1. Cejas jesus “compensatory fuzzy logic”,la habana revista de Ingenieria industrial .2011
  2. Valiant leslie “natures algorithms for learning and prospering in complex world “, New york 2013
  3. Han, M. Kamber and J. Pei, “Data Mining: Concepts and Techniques,” Morgan Kauf-mann, San Francisco, 2006. pp. 325-370.
  4. E. lughoter “envloing fuzzy system:methodologies, advanced concept and application”.springer heidelberg 2011.
  5. S. S. Haykin, “Neural Networks and Learning Machines,” 3rd Ed., Prentice Hall, New York, 2009.
  6. J. Han, M. Kamber and J. Pei, “Data Mining: Concepts and Techniques,” Morgan Kauf-mann, San Francisco, 2006. pp. 325-370.
  7. K. Subramanian, R. Savitha and S. Suresh, “Complex-valued Networks(IJCNN), The 2012 International Joint Conference on. IEEE, 2012. pp. 1-7.
  8. T. Kondo, J. Ueno and S. Takao, “Hybrid feedback GMDH- type neural network self-selecting various neurons and its application to medical image diagnosis of lung cancer,” In: Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on. IEEE, 2012. pp. 1925-1930
  9. A. Gajate, R. E. Haber, P. I. Vega and J. R. “Alique A transductive neuro-fuzzy controller: application to a drilling process, Neural Networks, IEEE July 2010, Transactions , vol. 21, no. 7, pp. 1158-1167,.
  10. H. Rouabah, C. Abdelmoula and M. Masmoudi, “Behavior control of a mobile robot based on Fuzzy logic and Neuro Fuzzy approaches for monitoring wall,” In: Design & Technology
  11. Narissara Eiamkanitchat and Poonarin Wongchomphu “Enhance Neuro-Fuzzy System for Classification using Dynamic Clustering”, The 4th Joint International Conference on Information and Communication Technology Electronic and Electrical Engineering (JICTEE-2014).
Index Terms

Computer Science
Information Sciences

Keywords

Neuro-fuzzy Classification Dynamic Clustering Class based grouping homogenous clustering.

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