The variable precision rough set inductive logic programming model and web usage graphs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2253, Issue , 2001, Pages 339-343

  Maheswari, V Uma ^a   Siromoney, Arul ^a   Mehata, K M ^a  

^a ANNA UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84974663980     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45548-5_42     Document Type: Conference Paper

References (11)

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4. A. Siromoney and K. Inoue. The generic Rough Set Inductive Logic Programming (gRS-ILP) model. In T.Y. Lin et al, editor, Granular Computing and Data Mining. Physica Verlag, 1999.
5. W. Ziarko. Variable precision rough set model. Journal of Computer and System Sciences, 46(1):39-59, 1993.
6. V. Uma Maheswari, Arul Siromoney, K. M. Mehata, and K. Inoue. The VariablePrecision Rough Set Inductive Logic Programming Model and Strings. Computational Intelligence, 2001. Accepted for publication.
7. A. Siromoney and K. Inoue. Elementary sets and declarative biases in a restricted gRS-ILP model. Informatica, 24:125-135, 2000.
8. S. Muggleton and C. Feng. Efficient induction in logic programs. In S. Muggleton,editor, Inductive Logic Programming, pages 281-298. Academic Press, 1992.
9. V. Uma Maheswari, Arul Siromoney, and K. M. Mehata. Mining webusage graphs. In Knowledge Based Computer Systems (KBCS2000), pages 186-192. Allied Publishers Limited, 2000.
10. José Borges and Mark Levene. Mining association rules in hypertext databases. In Proc. Fourth Int. Conf. on Knowledge Discoveryand Data Mining, pages 149-153, 1998.
11. Mike Perkowitz and Oren Etzioni. Towards adaptive websites: Concep tualframework and case study. In The Eighth Int. World Wide Web Conference, Toronto, Canada, May 1999.