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Reseach Article

Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization

by V.l. Kartheek, V. Chandra Sekhar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 13
Year of Publication: 2014
Authors: V.l. Kartheek, V. Chandra Sekhar
10.5120/18812-0388

V.l. Kartheek, V. Chandra Sekhar . Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization. International Journal of Computer Applications. 107, 13 ( December 2014), 20-26. DOI=10.5120/18812-0388

@article{ 10.5120/18812-0388,
author = { V.l. Kartheek, V. Chandra Sekhar },
title = { Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 13 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number13/18812-0388/ },
doi = { 10.5120/18812-0388 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:58.774482+05:30
%A V.l. Kartheek
%A V. Chandra Sekhar
%T Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 13
%P 20-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a days, Text clustering becomes an important application to organize the data and to extract useful information from the available corpus. Many previous clustering techniques have difficulties in handling extreme outliers but fuzzy clustering algorithms tend to give them very small membership degree in surrounding clusters. In this paper we proposed an aggregated probabilistic Fuzzy relational sentence level expectation maximization clustering algorithm for efficient text categorization. It will give the accurate and maximum similarity by finding the relevance of sentences which belongs to a particular cluster. This technique leads to a fuzzy partition of the sentences and find out the accurate probability of the words belongs to a cluster. This algorithm is particularly used in finding maximum likelihood estimates of words in a given sentence. It gives the low search results with highest accuracy. The practical results show that the proposed method obtains better and accurate results for getting best sentence-wise text classification when compared with the existing methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy clustering corpus outliers.