Two-Way Clustering for Contingency Tables: Maximizing a Dependence Measure-Reference-Cited by-同舟云学术

Two-Way Clustering for Contingency Tables: Maximizing a Dependence Measure

Published:2003 Issue: Volume: Page:143-154
ISSN:1431-8814
Container-title:Between Data Science and Applied Data Analysis
language:
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Author:

Bock Hans-Hermann

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-18991-3_17

Reference15 articles.

1. BOCK, H.-H. (1968): Statistische Modelle für die einfache und doppelte Klassifikation von normalverteilten Beobachtungen. Dissertation, Univ. of Freiburg, 1968

2. BOCK, H.-H. (1972): Statistische Madelle und Bayes’sche Verfahren zur Bestimmung einer unbekannten Klassifikation normalverteilter zufälliger Vektoren. Metrika 18, 120–132

3. BOCK, H.-H. (1974): Automatische Klassifikation. Mathematische und statistische Methoden zur Strukturierung von Daten (Clusteranalyse). Vandenhoeck & Ruprecht, Göttingen.

4. BOCK, H.-H. (1983): A clustering algorithm for choosing optimal classes for the chi-squared test. Bull. Intern. Statist. Inst., 44th Session, Madrid 1983, Vol. II: Contributed papers, 758–762

5. BOCK, H.-H. (1992): A clustering algorithm for maximizing ø-divergence, non-centrality and discriminating power. In: M. Schader (ed.): Analyzing and modeling data and knowledge. Springer-Verlag, Heidelberg, 1991, 19–36

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