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Reseach Article

Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network

by Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 7
Year of Publication: 2012
Authors: Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma
10.5120/4979-7237

Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma . Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network. International Journal of Computer Applications. 40, 7 ( February 2012), 38-42. DOI=10.5120/4979-7237

@article{ 10.5120/4979-7237,
author = { Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma },
title = { Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 7 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number7/4979-7237/ },
doi = { 10.5120/4979-7237 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:28.329576+05:30
%A Ram Prakash Gangwar
%A Jitendra Agrawal
%A Varsha Sharma
%T Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 7
%P 38-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification and Association rule mining are two basic tasks of Data Mining. Classification rules mining finds rules that partition the data into disjoint sets. This paper is based on MrCAR (Multi-relational Classification Algorithm) and Kohonen’s Self-Organizing Maps (SOM) approach. SOM is a class of typical artificial neural networks (ANN) with supervised learning which has been widely used in classification tasks. For small disjunction mining, we collocate with a new auto level threshold generation method in our algorithm to solve the problem of unclassified data of MrCAR. So, we optimize the classification rate of MrCAR with SOM network and improve the efficiency of classification. This approach is highly effective for classification of various kinds of databases and has better average classification accuracy in comparison with MrCAR. Finally the results convincingly demonstrated that our proposed algorithm has high accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Data mining MrCAR and SOM