Rough sets used in the measurement of similarity of mixed mode data

Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Volumn 2003-January, Issue , 2003, Pages 197-201

  Coppock, S ^a   Mazlack, L ^a  

^a University of Cincinnati   (United States)

Author keywords

Artificial intelligence; Cities and towns; Clustering algorithms; Data analysis; Frequency; Laboratories; Measurement uncertainty; Rough sets; Set theory; Unsupervised learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DATA REDUCTION; INFORMATION SCIENCE; LABORATORIES; SET THEORY; UNCERTAINTY ANALYSIS; UNSUPERVISED LEARNING;

CITIES AND TOWNS; DISSIMILARITY ANALYSIS; FREQUENCY; MEASUREMENT UNCERTAINTY; MEASURING SIMILARITIES; MIXED-MODE DATA; QUALITATIVE DATA; SIMILARITY METRICS;

ROUGH SET THEORY;

EID: 79551646468     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NAFIPS.2003.1226781     Document Type: Conference Paper

References (6)

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4. Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Dordrecht, 1991.
5. Sneath, P and Sokal, R. Numerical Taxonomy, W. H. Freeman, San Francisco, 1973.
6. Zhu, Y., Unsupervised Database Discovery Based on Artificial Intelligence Techniques, Masters Thesis, University of Cincinnati, June, 2002, 107 pg.