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Reseach Article

Review on Classification and Clustering using Fuzzy Neural Networks

by Suprit Kulkarni, Kishore Honwadkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 3
Year of Publication: 2016
Authors: Suprit Kulkarni, Kishore Honwadkar
10.5120/ijca2016908456

Suprit Kulkarni, Kishore Honwadkar . Review on Classification and Clustering using Fuzzy Neural Networks. International Journal of Computer Applications. 136, 3 ( February 2016), 18-23. DOI=10.5120/ijca2016908456

@article{ 10.5120/ijca2016908456,
author = { Suprit Kulkarni, Kishore Honwadkar },
title = { Review on Classification and Clustering using Fuzzy Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 3 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number3/24133-2016908456/ },
doi = { 10.5120/ijca2016908456 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:02.214510+05:30
%A Suprit Kulkarni
%A Kishore Honwadkar
%T Review on Classification and Clustering using Fuzzy Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 3
%P 18-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In data mining two important tasks involved are classification and clustering. In general, in classification the classifier assigns a class label from a set of predefined classes to a new input object. Whereas, given a set of objects, clustering creates different groups of these objects using some similarity measure. In the context of machine learning, classification is supervised learning and clustering is unsupervised learning. There are different approaches used for classification and clustering. In recent past many fuzzy neural networks have been proposed which can be employed for classification and clustering. Unlike other techniques, the fuzzy neural networks are quickly trainable, suitable for online training, provides soft decision, and capable of constructing nonlinear decision boundaries. All these benefits make them suitable for difficult real world problems involving classification and clustering. This paper provides review on recent fuzzy neural learning algorithms and mainly focusing on pattern/object classification and clustering.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Clustering Fuzzy Neural Network