Classification of web documents using fuzzy logic categorical data clustering

IFIP International Federation for Information Processing

Volumn 247, Issue , 2007, Pages 93-100

  Tsekouras, George E ^a   Anagnostopoulos, Christos ^a   Gavalas, Damianos ^a   Dafhi, Economou ^a  

^a UNIVERSITY OF THE AEGEAN   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; FUZZY CLUSTERING; FUZZY LOGIC; HAMMING DISTANCE; WORLD WIDE WEB;

CATEGORICAL DATA; CATEGORICAL DATA CLUSTERING; CLUSTER CENTERS; DISSIMILARITY MEASURES; MINIMUM DISTANCE; NUMBER OF CLUSTERS; WEB DOCUMENT;

INFORMATION RETRIEVAL SYSTEMS;

EID: 36249007855     PISSN: 15715736     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-0-387-74161-1_11     Document Type: Conference Paper

References (15)

1. Smith, K.A, and Ng, A.: Web page clustering using a self-organizing map of user navigation patterns, Decision Support Systems 35 (2003) 245-256
2. Macskassy, S. A., Banerjee, A., Davison, B. D., and Hirsh, H.: Human performance on clustering web pages: A preliminary study, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (1998)
3. Anagnostopoulos, I., Anagnostopoulos, C, Loumos, V., and Kayafas, E.: Classifying web pages employing a probabilistic neural network, lEE Proceedings on Software 151(3) (2004) 139-150
4. Qi, D., and Sun, B.: A genetic k-means approach for automated web page classification. Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration (2004) 241-246
5. Jain, A.K.,AMurty, M.N., and Flynn, PJ.: Data clustering: a review, ACM Computing Survey 31(3) (199) 264-323
6. Runkler, T.A., and Bezdek, J.C.: Web mining with relational clustering. International Journal of Approximate Reasoning 32 (2003) 217-236
7. Bezdek, J.C., and Pal, K.: FuzzyMmodels for pattern recognition: methods that search for structures in data, IEEE Press (1992), New York, NY
8. Manning, CD., and Schutze, H.: Foundations of statistical natural language processing, MIT Press (1999), Cambridge, MA
9. Punin, J.R., Krishnamoorthy, M.S, and Zaki, M.J.: Web usage mining-languages and algorithms. Technical Report, Rensselaer Polytechnic Institute, NY (2001)
10. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals, Sov. Phys. Dokl. 6 (1966) 705-710
11. Z. Huang, and M. K. Ng, A fuzzy k-modes algorithm for clustering categorical data, IEEE Transactions on Fuzzy Systems, Vol. 7, no 4, 1999, pp. 446-452
12. Tsekouras, G. E., Papageorgiou, D., Kotsiantis, S., Kalloniatis, C, and Pintelas, P.: Fuzzy Clustering of Categorical Attributes and its Use in Analyzing Cultural Data, International Journal of Computational Intelligence 1(2) (2004) 147-151
13. Chen, J., Miculcic, A., and Kraft, D.H.: An integrated approach to information retrieval with fuzzy clustering and inferencing, in Knowledge Management in Fuzzy DataBases, Pons, O., Vila, M.A., and Kacprzyk, J. (Eds), Physic Veriag, Vol. 163 (2000)
14. Jones, K.S.: A statistical interpretation of tem specificity and its application in retrieval, J. Domument 28(1) (1972) 11-20
15. Huang, Z.: Extensions of the k-means algorithm for clustering large data sets with categorical values, Data Mining and Knowledge Discovery 2 (1998) 283-304