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Reseach Article

Efficient Clustering for Cluster based Boosting

Published on May 2016 by Yogesh D. Ghait
National Conference on Advancements in Computer & Information Technology
Foundation of Computer Science USA
NCACIT2016 - Number 4
May 2016
Authors: Yogesh D. Ghait
58948735-6a4a-4475-9f68-de70ed03b70f

Yogesh D. Ghait . Efficient Clustering for Cluster based Boosting. National Conference on Advancements in Computer & Information Technology. NCACIT2016, 4 (May 2016), 23-25.

@article{
author = { Yogesh D. Ghait },
title = { Efficient Clustering for Cluster based Boosting },
journal = { National Conference on Advancements in Computer & Information Technology },
issue_date = { May 2016 },
volume = { NCACIT2016 },
number = { 4 },
month = { May },
year = { 2016 },
issn = 0975-8887,
pages = { 23-25 },
numpages = 3,
url = { /proceedings/ncacit2016/number4/24722-3070/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancements in Computer & Information Technology
%A Yogesh D. Ghait
%T Efficient Clustering for Cluster based Boosting
%J National Conference on Advancements in Computer & Information Technology
%@ 0975-8887
%V NCACIT2016
%N 4
%P 23-25
%D 2016
%I International Journal of Computer Applications
Abstract

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Boosting is the iterative process which aims to improve the predictive accuracy of the learning algorithms. Clustering with boosting improves quality of mining process. When supervised algorithms applied on training data for learning, there may be possibility of biased learning which affects the accuracy of prediction result. Boosting provides the solution for this. It generates subsequent classifiers by learning incorrect predicted examples by previous classifier. Boosting process possesses some limitations. Different approaches introduced to overcome the problems in boosting such as overfitting and troublesome area problem to improve performance and quality of the result. Cluster based boosting address limitations in boosting for supervised learning systems. In literature Cluster based boosting [6] is used to address limitations in boosting for supervised learning systems. In paper [6], k-means is used as a clustering algorithm. Encapsulation of another clustering method with CBB may result into increase in the performance. In our proposed work we used fuzzy c means (FCM), Expectation Minimization and Hierarchical algorithm with CBB and compared the results.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Classification Boosting decision Tree