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Design and Comparison of Agglomerative Hierarchical Clustering

by Sarika, Mukesh Rawat
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 10
Year of Publication: 2017
Authors: Sarika, Mukesh Rawat
10.5120/ijca2017914993

Sarika, Mukesh Rawat . Design and Comparison of Agglomerative Hierarchical Clustering. International Journal of Computer Applications. 172, 10 ( Aug 2017), 1-5. DOI=10.5120/ijca2017914993

@article{ 10.5120/ijca2017914993,
author = { Sarika, Mukesh Rawat },
title = { Design and Comparison of Agglomerative Hierarchical Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 10 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number10/28284-2017914993/ },
doi = { 10.5120/ijca2017914993 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:56.976177+05:30
%A Sarika
%A Mukesh Rawat
%T Design and Comparison of Agglomerative Hierarchical Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 10
%P 1-5
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As more and more documents are available in the form of hypertext in world wide web, proper clustering of documents are required for generating the similar results fetched by the search engine specific to a user entered search query as the documents within the cluster are similar to each other. In Agglomerative approach of clustering the clusters of documents are merged into cluster unless until all the clusters belong to a root cluster .In this paper design and comparison of two agglomerative hierarchical clustering of documents is done on parameters such as cluster generation, relevance of result get against a query and purity of clusters.

References
  1. Nicholas O. Andrews and Edward A. Fox, “Recent Developments in Document Clustering”, thesis, October 16, 2007.
  2. Jain and R. Dubes. “Algorithms for Clustering Data.” Prentice Hall, 1988.
  3. Chris Staff: Bookmark Category Web Page Classification Using Four Indexing and Clustering Approaches. AH 2008:345-348.
  4. Han J., Kamber M.,”Data Mining: Concepts and Techniques,” Morgan Kaufmann (Elsevier),2006.
  5. Seung-sikh, “Keyword based document clustering”, report, school of cs, kookim university. Seoul, Korea.
  6. Swatantra Kumar Sahu*, “Classification of Document clustering Approaches”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 2, Issue 5, May 2012.
  7. Charu C. Aggarwal, “A Survey of Text Clustering Algorithms”, report, IBM T. J. Watson Research Center Yorktown Heights, NY.
  8. Anna Huang, “Similarity Measures for Text Document Clustering”, report, Department of Computer Science, The University of Waikato, Hamilton, NewZealand.
Index Terms

Computer Science
Information Sciences

Keywords

Agglomerative relevance purity cluster search engine.

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