CFP last date
20 January 2025
Reseach Article

Indexing in Search Engines based on Pipelining Architecture using Single Link HAC

by Anuradha Tyagi, Khaleel Ahmad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 19
Year of Publication: 2012
Authors: Anuradha Tyagi, Khaleel Ahmad
10.5120/7876-0923

Anuradha Tyagi, Khaleel Ahmad . Indexing in Search Engines based on Pipelining Architecture using Single Link HAC. International Journal of Computer Applications. 49, 19 ( July 2012), 12-16. DOI=10.5120/7876-0923

@article{ 10.5120/7876-0923,
author = { Anuradha Tyagi, Khaleel Ahmad },
title = { Indexing in Search Engines based on Pipelining Architecture using Single Link HAC },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 19 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number19/7876-0923/ },
doi = { 10.5120/7876-0923 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:37.360691+05:30
%A Anuradha Tyagi
%A Khaleel Ahmad
%T Indexing in Search Engines based on Pipelining Architecture using Single Link HAC
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 19
%P 12-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Search on the web is a daily activity for many people through- out the world. Applications based on search are everywhere. We are in a data rich situation this becomes an obstacle for Information retrieval system. In this paper, we propose a pipelining architecture of indexing in order to enhance memory utilization and fast query optimization and also a soft clustering algorithm is applied to group documents into clusters hierarchically linked. Each cluster is labeled with is most relevant term known as document identifier. Such that documents within the same cluster are similar. In this way it will create hierarchy of index so that search will travel from lower level to higher level. The comparisons of the document against the user query will direct to specific clusters not the whole collection of clusters.

References
  1. Grossman, Frieder, Goharian. IR Basics of Inverted Index. 2002. Verified Dec 2006.
  2. Anand Rajaraman,Jeffrey D. Ullman, "Mining of massive data sets" Stanford University 2010-11
  3. J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297
  4. Athman Bouguettaya "On Line Clustering", IEEE Transaction on Knowledge and Data Engineering Volume 8, No. 2, April 1996.
  5. Sergey Melnik, Sriram Raghavan, Beverly Yang, and Hector Garcia-Molina. Building a Distributed Full–Text Index for the Web. In World Wide Web, pages 396–406,2001
  6. Parul Gupta, Dr. A. K. Sharma, Hierarchical Clustering based Indexing in Search Engines, communicated to International Journal of Information and Communication Technology.
  7. Sanjiv K. Bhatia. "Adaptive K-Means Clustering" American Association for Artificial Intelligence, 2004. .
  8. Oren zamir''Web document Clustering''department of Computer science and engineering,University of Washington. 2007
  9. Yi Zhang and Tao li ''A Framework for evaluating and organizing document clustering through visualization'' published by ACM, USA.
Index Terms

Computer Science
Information Sciences

Keywords

Agglomerative Clustering Pipelining Hierarchical Indexing