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Reseach Article

Empirical Investigation of Finding Person in Social Network using Clustering

by Trupti S. Indi, Raj B. Kulkarni, S.K.Dixit
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 5
Year of Publication: 2011
Authors: Trupti S. Indi, Raj B. Kulkarni, S.K.Dixit
10.5120/2356-3082

Trupti S. Indi, Raj B. Kulkarni, S.K.Dixit . Empirical Investigation of Finding Person in Social Network using Clustering. International Journal of Computer Applications. 19, 5 ( April 2011), 29-34. DOI=10.5120/2356-3082

@article{ 10.5120/2356-3082,
author = { Trupti S. Indi, Raj B. Kulkarni, S.K.Dixit },
title = { Empirical Investigation of Finding Person in Social Network using Clustering },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 5 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number5/2356-3082/ },
doi = { 10.5120/2356-3082 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:13.338649+05:30
%A Trupti S. Indi
%A Raj B. Kulkarni
%A S.K.Dixit
%T Empirical Investigation of Finding Person in Social Network using Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 5
%P 29-34
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We are trying to identify person as an entity from social network data by grouping the person with some characteristics. It is very difficult to identify people on social networks especially for specific names in different cultures like India, and other ethnic places whose names are difficult to trace. This is a small effort to bridge the gap of identifying person with features like name, date of birth and address containing state, city etc. We are trying to make use of clustering algorithms to uniquely identify people/person on social networks. An empirical analysis of the same is presented here.

References
  1. JAIN A.K., MURTY M.N., and FLYNN P.J.: Data Clustering: A Review (ACM Computing Surveys, Vol. 31, No. 3, September 1999)
  2. Margaret H. Dunham Data Mining Introduction & Advanced Topics (Pearson Education, 2006)
  3. Guha Sudipto, Rastogi Rajeev, and Kyuseok Shim: CURE: An Efficient Clustering Algorithm for Large Databases
  4. Teevan Jaime, Morri Meredith Ringel, and Bush Steve: Discovering and Using Groups to Improve Personalized Search
  5. Culotta Aron, Bekkerman Ron, and McCallum Andrew: Extracting social networks and contact information from email and the Web (2004)
  6. Matsuo Yutaka, Mori Junichiro, Hamasaki Masahiro, Nishimura Takuichi, Takeda Hideaki, Hasida Koiti, and Ishizuka Mitsuru: An advanced social network extraction system from the Web
  7. Kautz H., Selman B., and Shah M.: The hidden Web. AI magazine, Vol. 18, No. 2, pp. 27–35, 1997
  8. Kurki Jussi: Finding People and Organizations on the Semantic Web
  9. FISHER DOUGLAS H.: Knowledge Acquisition via Incremental Conceptual Clustering (Machine Learning 2: 139-172, 1987)
  10. Chan Stefani, Pon Raymond K., and C´ardenas Alfonso F.: Visualization and Clustering of Author Social Networks
Index Terms

Computer Science
Information Sciences

Keywords

Social network clustering features CURE