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Enhanced Performance of Search Engine with Multitype Feature Co-selection of Fuzzy K-Means Clustering Algorithm

by K. Parimala, V. Palanisamy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 11
Year of Publication: 2013
Authors: K. Parimala, V. Palanisamy
10.5120/11127-6200

K. Parimala, V. Palanisamy . Enhanced Performance of Search Engine with Multitype Feature Co-selection of Fuzzy K-Means Clustering Algorithm. International Journal of Computer Applications. 66, 11 ( March 2013), 12-15. DOI=10.5120/11127-6200

@article{ 10.5120/11127-6200,
author = { K. Parimala, V. Palanisamy },
title = { Enhanced Performance of Search Engine with Multitype Feature Co-selection of Fuzzy K-Means Clustering Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 11 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number11/11127-6200/ },
doi = { 10.5120/11127-6200 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:22:05.853169+05:30
%A K. Parimala
%A V. Palanisamy
%T Enhanced Performance of Search Engine with Multitype Feature Co-selection of Fuzzy K-Means Clustering Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 11
%P 12-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Information world meet many confronts nowadays and one such, is data retrieval from a multidimensional and heterogeneous data set. Han & et al carried out a trail for the mentioned challenge. A novel feature co-selection for Web document clustering is proposed by them, which is called Multitype Features Co-selection for Clustering (MFCC). MFCC uses intermediate clustering results in one type of feature space to help the selection in other types of feature spaces. It reduces effectively of the noise introduced by "pseudoclass" and further improves clustering performance. This efficiency also can be used in data retrieval, by implementing the MFCC algorithm in ranking algorithm of search engine technique. The proposed work is to implement the fuzzy MFCC algorithm in search engine architecture. Such that the information retrieves from the data-set is retrieved effectively and shows the relevant retrieval.

References
  1. Ed. Green grass, "Information Retrieval: A survey"; 2000.
  2. Report from SWIR 2012; "Frontiers, Challenges, and Opportunities for IR"; ACM SIGIR forum vol. 46, No. 1, June 2012.
  3. Sew Staff, "How search engines work", 2007.
  4. Han & et al. , "Multi type feature co-selection for clustering for web documentation", IEEE transaction on knowledge engineering, June 2006.
  5. K. Parimala, Dr. V. Palanisamy, "Enhanced Performance of Search Engine with Multi-Type Feature Co-Selection for Clustering Algorithm", International Journal of Computer Applications (0975 - 8887) Volume 53- No. 7, September 2012
  6. K. Parimala, Dr. V. Palanisamy, "Implementation of Fuzzy K-Means in Multi-Type Feature Coselection for Clustering", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-5, November 2012.
Index Terms

Computer Science
Information Sciences

Keywords

MFCC algorithm Search Engine-Ranking algorithm

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