ZigZag, a new clustering algorithm to analyze categorical variable cross-classification tables

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

Volumn 1704, Issue , 1999, Pages 398-405

  Lallich, Stéphane ^a  

^a UNIVERSITÉ LUMIÈRE LYON 2   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING;

CATEGORICAL VARIABLES; CONDITIONAL MEANS; CORRELATION RATIO; EQUIVALENCE RELATIONS; NEW CLUSTERING ALGORITHMS; NOMINAL VARIABLES; PREDICTION RULES; QUANTITATIVE DATA;

CLUSTERING ALGORITHMS;

EID: 84956861132     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-48247-5_49     Document Type: Conference Paper

References (7)

1. Agresti A. (1990), Categorical Data Analysis, John Wiley, New-York.
2. Bergeron S., Lallich S., Le Bas C., (1998), Location of Inovative Activities and Technological Structure in the French Economy, l985-90: Some Evidences from U.S patenting, Research Policy, 26, pp. 733-75l.
3. Chauchat J.H., Risson A. (1998),” Bertin’s Graphics and Multidimensional Data Analysis”, in Blasius J., Greenacre M. (1998),” Visualization of Categorical Data”, Academic Press
4. Goodman L.A., Kruskal W.H.(1954), Measures of Association for Cross Classifications I, JASA, 49, pp. 732-764.
5. Guillien F. (1998), Mise en oeuvre de ZigZag sous Delphi: Datamix, Memoire de Maîtrise Sciences Economiques, Universite Lumiere Lyon 2.
6. Lallich S. (1999), Concept de diversite et association predictive, SFDS 99, Grenoble.
7. Rakotomalala R., Lallich S. (1998), Handling Noise with Generalized Entropy of Type Beta in Induction Graphs Algorithms, JCIS’98, Duke, USA.