A framework for statistical clustering with a constant time approximation algorithms for K-median clustering

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3120, Issue , 2004, Pages 415-426

  Ben David, Shai ^a,b  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b Department of Obstetrics Gynecology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DISTRIBUTIONS; REGRESSION ANALYSIS; THEOREM PROVING; VECTOR QUANTIZATION; APPROXIMATION ALGORITHMS;

EUCLIDEAN DIMENSION; K-MEDIAN CLUSTERING; LINEAR REGRESSION; OPTIMAL CLUSTERING; ACCURACY PARAMETERS; ARBITRARY DISTRIBUTION; CLUSTERING PROBLEMS; CONSTANT-TIME APPROXIMATIONS; EUCLIDEAN DIMENSIONS; SAMPLING-BASED ALGORITHMS; STATISTICAL CLUSTERING;

STATISTICAL METHODS; CLUSTERING ALGORITHMS;

EID: 9444254174     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-27819-1_29     Document Type: Conference Paper

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