FUZZY CLUSTERING TECHNIQUE

Authors

  • Anjali B. Raut Department of Computer Science & Engg, HVPM’s COET, Amravati, India Author
  • G. R. Bamnot Department of Computer Science & Engg, PRMITR ,Badner, Amravati ,India Author

Keywords:

Web Mining, Clustering, Search Engine, Fuzzy clustering

Abstract

Fuzzy clustering techniques are used to construct clusters with uncertain boundaries and allows that one object belongs to overlapping clusters with some membership degree. In other words, the fuzzy clustering is to consider not only the belonging status of object to the clusters, but also to consider to what degree do the object belong to the cluster. In this paper, a technique called “fuzzy hierarchical clustering” is being proposed that creates the clusters of web documents using fuzzy equivalence relation.

References

http://news.netcraft.com

WangBin and LiuZhijing , Web Mining Research , In Proceeding of the 5th International Conference on Computational Intelligence and Multimedia Applications(ICCIMA’03) 2003.

Oren Etzioni, The World Wide Web: quagmire or gold mine? ,Communications of ACM”, Nov 96.

R. Cooley,B. Mobasher and J. Srivastava ,Web Mining: Information and Pattern Discovery on the World Wide Web, In the Proceeding of ninth IEEE International Conference on Tools with Artificial Intelligence(ICTAI’97),1997.

Hillol Kargupta, Anupam Joshi, Krishnamoorthy Sivakumar and Yelena Yesha,Data Mining: Next Generation Challenges and Future Directions, MIT Press,USA , 2004.

R. Kosala and H.Blockeel, Web Mining Research: A Survey, SIGKDD Explorations ACM SIGKDD, July 2000.

Sankar K. Pal,Varun Talwar and Pabitra Mitra , Web Mining in Soft Computing Framework : Relevance, State of the Art and Future Directions , IEEE Transactions on Neural Network , Vol 13,No 5,Sept 2002 .

Andreas Hotho and Gerd Stumme, Mining the World Wide Web- Methods, Application and Perceptivities, in Künstliche Intelligenz, July 2007.

C.M. Benjamin, K.W. Fung, and E. Martin, Encyclopaedia of DataWarehousing and Mining. Montclair State University, USA. 2006.

A. Jain, and M. Murty, Data Clustering: A Review ACM Computing Surveys, vol. 31, pp. 264-323. 1999.

E.Z. Oren, Clustering Web Documents: A Phrase-Based Method for Grouping Search Engine Results, Ph.D. Thesis, University of Washington.1999.

O. Zamir, O. Etzioni, Web Document Clustering, Department of Computer Science and Engineering, University of Washington, Proceedings of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 46-54.1998.

S. Sambasivam, and N. Theodosopoulos, Advanced Data Clustering Methods of Mining Web Documents. Issues in Informing Science and Information Technology. Vol. 3, pp. 563-579.

Y.M. Cheung, K*-means: A New Generalized k-means ClusteringAlgorithm. Pattern Recognition Letters, vol. 24, pp. 2883-2893.2003.

Z. Huang, Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values.Data Mining and Knowledge Discovery, vol. 2, pp. 283-304.1998

E. Diday, The Dynamic Cluster Method in Non-Hierarchical Clustering. Journal of Computer Information Science. Vol. 2, pp. 61-88. 1973.

M.J. Symon, Clustering Criterion and Multi-Variate Normal Mixture.Biometrics, vol. 77, pp. 35-46. 1977.

A. K. Jain,M. N. Murty and P. J. Flynn, Data clustering: A review, ACM computing surveys 31(3):264-323,Sept 1999.

King-Ip Lin and Ravikumar Kondadadi, A Similarity Based Soft Clustering Algorithm for Documents, in Proceeding of the 7th International Conference on Database Systems for Advanced Applications DASFAA-2001), April 2001.

Published

2011-01-10

How to Cite

Anjali B. Raut, & G. R. Bamnot. (2011). FUZZY CLUSTERING TECHNIQUE. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 1(1), 01-09. https://ijcserd.in/index.php/home/article/view/IJCSERD_01_01_001