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Reseach Article

Categorical Data Clustering using Cosine based similarity for Enhancing the Accuracy of Squeezer Algorithm

by R.ranjani, S.anitha Elavarasi, J.akilandeswari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 20
Year of Publication: 2012
Authors: R.ranjani, S.anitha Elavarasi, J.akilandeswari
10.5120/7036-9705

R.ranjani, S.anitha Elavarasi, J.akilandeswari . Categorical Data Clustering using Cosine based similarity for Enhancing the Accuracy of Squeezer Algorithm. International Journal of Computer Applications. 45, 20 ( May 2012), 41-45. DOI=10.5120/7036-9705

@article{ 10.5120/7036-9705,
author = { R.ranjani, S.anitha Elavarasi, J.akilandeswari },
title = { Categorical Data Clustering using Cosine based similarity for Enhancing the Accuracy of Squeezer Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 20 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number20/7036-9705/ },
doi = { 10.5120/7036-9705 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:38:08.431711+05:30
%A R.ranjani
%A S.anitha Elavarasi
%A J.akilandeswari
%T Categorical Data Clustering using Cosine based similarity for Enhancing the Accuracy of Squeezer Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 20
%P 41-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

DISC Measure, Squeezer, Categorical Data Clustering, Cosine similarity

References
  1. Rishi Sayal and Vijay Kumar. V. 2011. A novel Similarity Measure for Clustering Categorical Data Sets. International Journal of Computer Application (0975-8887).
  2. Aditya Desai, Himanshu Singh and Vikram Pudi. 2011. DISC Data-Intensive Similarity Measure for Categorical Data. Pacific-Asia Conferences on Knowledge Discovery Data Mining.
  3. Shyam Boriah, Varun Chandola and Vipin Kumar. 2008. Similarity Measure for Clustering Categorical Data. Comparative Evaluation. SIAM International Conference on Data Mining-SDM.
  4. Taoying Li, Yan Chen. 2009. Fuzzy Clustering Ensemble Algorithm for partitional Categorical Data. IEEE, International conference on Business Intelligence and Financial Engineering.
  5. HE Zengyou, XU Xiaofei. 2002. SQUEEZER: An Efficient Algorithm for Clustering Categorical Data. Vol. 17 No. 5. Journal on Computer Science & Technology.
  6. D. Arthur and S. Vassilvitskii. 2007. K-means++: The advantages of careful seeding. In Proc. 18th Annu. ACM-SIAM Symp. Discrete Algorithm.
  7. Z. Haung and Michael K. Ng. 1999. A Fuzzy k-Modes Algorithm for Clustering Categorical Data. IEEE Transaction On Fuzzy systems, Vol. 7, No-4.
  8. Wang Jiacai and Gu Ruijun. 2010. An Extended Fuzzy K-Means Algorithm for Clustering Categorical Valued Data. International Conference on Artificial Intelligence and Computational Intelligence.
  9. Hua Yan, Keke Chen, Ling Liu3, Zhang Yil. 2009. SCALE: A Scalable Framework for Efficiently Clustering Transactional Data. Data mining and knowledge Discovery.
  10. Zengyou He, Xiaofei Xu,Shenchun Deng. 2008. k-ANMI: A Mutual Induction Based Clustering Algorithm for Categorical Data. Information Fusion9 (2).
  11. Zengyou He, Xiaofei Xu,Shenchun Deng . 2006. Improving Categorical Data Clustering Algorithm by Weighting Uncommon Attribute Value Matches. ComSIS Vol. 3, No. 1.
  12. Zengyou He, Xiaofei Xu. 2005. Scalable Algorithm for Clustering Large Dataset with Mixed Type Attributes. International Journal of Intelligent Systems. Vol. 20.
  13. P. Gambaryan. 1964. A Mathematical Modeling of taxonomy. Izvest. Akad. Nauk Armen. SSR, 17(12).
  14. Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Second Edition Morgan Kaufmann publishers.
Index Terms

Computer Science
Information Sciences

Keywords

Disc Measure Squeezer Categorical Data Clustering Cosine Similarity.