Hierarchical document clustering based on tolerance rough set model

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

Volumn 1910, Issue , 2000, Pages 458-463

  Kawasaki, Saori ^a   Nguyen, Ngoc Binh ^a   Ho, Tu Bao ^a  

^a JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; DATA MINING; INFORMATION RETRIEVAL; PETROLEUM RESERVOIR EVALUATION; ROUGH SET THEORY; SEMANTICS;

DOCUMENT CLUSTERING; DOCUMENT REPRESENTATION; HIERARCHICAL DOCUMENT; KNOWLEDGE DISCOVERY IN TEXTS; TEXT MINING; TOLERANCE ROUGH SETS;

CLUSTERING ALGORITHMS;

EID: 84974659934     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45372-5_51     Document Type: Conference Paper

References (5)

1. Fakes, W. B. and Baeza-Yates, Information Retrieval. Data Structures and Algorithms (eds.), Prentice Hall, 1992.
2. Ho, T. B. and Funakoshi K., "Information retrieval using rough sets", Journal of Japanese Society for Artificial Intelligence, Vol. 13, N. 3, 1998, 424-433.
3. Pawlak, Z., Rough sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, 1991.
4. Polkowski, L. and Skowron, A., Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems (eds.), Physica-Verlag, 1998.
5. Skowron, A. and Stepaniuk, J., "Generalized approximation spaces", The 3rd International Workshop on Rough Sets and Soft Computing, 1994, 156-163.