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An Advanced Fuzzy Constructing Algorithm for Feature Discovery in Text Mining

by Evana Ramalakshmi, Subhakar Golla
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 17
Year of Publication: 2015
Authors: Evana Ramalakshmi, Subhakar Golla

Evana Ramalakshmi, Subhakar Golla . An Advanced Fuzzy Constructing Algorithm for Feature Discovery in Text Mining. International Journal of Computer Applications. 127, 17 ( October 2015), 30-34. DOI=10.5120/ijca2015906720

@article{ 10.5120/ijca2015906720,
author = { Evana Ramalakshmi, Subhakar Golla },
title = { An Advanced Fuzzy Constructing Algorithm for Feature Discovery in Text Mining },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 17 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2015906720 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:18:41.346793+05:30
%A Evana Ramalakshmi
%A Subhakar Golla
%T An Advanced Fuzzy Constructing Algorithm for Feature Discovery in Text Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 17
%P 30-34
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

It is a big task to provide the accuracy of discovered relevance features in text documents for describing user requirements. Classification of data is biggest issue in more text documents because they have large number of words and data patterns. Most existing popular methods are used by word-based approaches. Still, they have all suffered from the problems of relevance and uncertainty. Over the years, there has been pattern-based methods should perform better result than word-based methods in describing user requirements. But, how to effectively use large scale patterns remains a typical problem in text mining. To overcome this problem, Fuzzy Relevance Feature Discovery Algorithm (FRFDA), classification techniques have been developed for relevance feature discovery. It describes both higher level and low level features based on word patterns. It is also classifies words into categories and updates those word weights based on their relevance and dispensation in patterns. The experimentation result proves that, the proposed FRFDA is better than existing manual and automation methods. The data set Reuters-21578 shows that the proposed model significantly outperforms faster and obtains better extracted features than other methods.

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Index Terms

Computer Science
Information Sciences


Text mining fuzzy similarity feature clustering text feature extraction text classification Fuzzy Relevance Feature Discovery (FRFD) Reuters Corpus Volume (RCV).