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Reseach Article

Using Fuzzy Center Mean (in general any) Clustering Methods to Construct Fuzzy Classifier Tuned to do Classification

by G. K. Vikram
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 4
Year of Publication: 2013
Authors: G. K. Vikram
10.5120/13386-1150

G. K. Vikram . Using Fuzzy Center Mean (in general any) Clustering Methods to Construct Fuzzy Classifier Tuned to do Classification. International Journal of Computer Applications. 77, 4 ( September 2013), 43-48. DOI=10.5120/13386-1150

@article{ 10.5120/13386-1150,
author = { G. K. Vikram },
title = { Using Fuzzy Center Mean (in general any) Clustering Methods to Construct Fuzzy Classifier Tuned to do Classification },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 4 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number4/13386-1150/ },
doi = { 10.5120/13386-1150 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:49:25.210315+05:30
%A G. K. Vikram
%T Using Fuzzy Center Mean (in general any) Clustering Methods to Construct Fuzzy Classifier Tuned to do Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 4
%P 43-48
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents about using FuzzyCentreMean[1][4][5][7][8] (in general any clustering method) to construct fuzzy classifier tuned to do classification. Clustering methods, in general try to form clusters of data in such a way that a huge chunk of data is reduced to its representative elements(sets). The different clustering methods are like different points of view of the same data[1][4][5][7][8]. For Fuzzy classifier decision making/logic is imparted by 'Rules of inferences' framed by expert human pertaining to data considered. This paper provides a way to construct the rules of inference without the need of humanly intervention but by interpreting data centers of the clusters[1][3][6][11] . The above case is mainly important in almost lossless data compression, in reconstruction of an entire image/video from the damaged copy and in areas of classification of data points into appropriate classes(similar to classes designed by humans manually after interpreting the data). The case study here, is on fisher Iris data set[9].

References
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Index Terms

Computer Science
Information Sciences

Keywords

fuzzy fuzzy clasifier cluster rules of inference data point data center membership mamdani type fuzzy center mean Fuzzy classifier algorithms