Download Conceptual and Numerical Analysis of Data: Proceedings of by Ingo Balderjahn (auth.), Prof. Dr. Otto Optiz (eds.) PDF

By Ingo Balderjahn (auth.), Prof. Dr. Otto Optiz (eds.)

The thirteenth convention of the Gesellschaft fUr Klassifikation e. V. came about on the Universitat Augsburg from April 10 to twelve, 1989, with the' neighborhood association via the Lehrstuhl fUr Mathematische Me­ thoden der Wirtschaftswissenschaften. The large ranged topic of the convention Conceptual and Numerical research of information used to be obliged to point the diversity of the recommendations of information and data in addition to the manifold tools of analysing and structuring. in keeping with the got bulletins of papers 4 sections were prepared: 1. facts research and category: simple options and strategies 2. functions in Library Sciences, Documentation and knowledge Sciences three. functions in Economics and Social Sciences four. purposes in normal Sciences and machine Sciences This type does not separate strictly, however it indicates that theo­ retic and making use of researchers of so much assorted disciplines have been disposed to provide a paper. In 60 survey and detailed lectures the audio system stated on advancements in conception and functions en­ couraging the interdisciplinary discussion of all contributors. This quantity comprises forty two chosen papers grouped based on the 4 sections. Now we supply a brief perception into the awarded papers. x a number of difficulties of proposal research, cluster research, information research and multivariate statistics are thought of in 18 pa­ pers of part 1. The geometric illustration of an idea lattice is a suite of figures within the aircraft equivalent to the given recommendations in the sort of means that the subconcept-superconcept-relation corresponds to the containment relation among the figures. R.

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Extra info for Conceptual and Numerical Analysis of Data: Proceedings of the 13th Conference of the Gesellschaft für Klassifikation e. V., University of Augsburg, April 10–12, 1989

Example text

E. 1. estimate J(C):= (JI,···,J m ) for a fixed C: m gm(C) := gm(C, J(C)) = - LL log f(Xkj Ji ) --. e. by substituting the m. 1. assignment partition C(tJ) = (G 1,···, Gm) with classes Gi . ax log f(Xkj tJ i ) --. 1c) (best location criterion). The same argumentation leads to the well-known k-means clustering algorithm (relaxation method, nuees dynamiquesj Schroeder 1976, Diday 1979, Chap. 12) where 19 gm is minimized with respect to fJ and C in turn: fJ(t) := J( C(t»), i := C(fJ(t») for t = 0,1,2,·· .

Amer. Statist. Assoc. 74 (1979) 708-714. , Thompson, J. W. Dispersive distributions, and the connection between dispersivity and strong unimodality. J. Appl. Prob. 18 (1981) 76-90. Li, L. , Sedransk, N. Mixtures of distributions: A topological approach. Ann. Statist. 16 (1988) 1623-1634. Lynch, J. Mixtures, generalized convexity and balayages. Scand. J. Statist. 15 (1988) 203-210. 41 Marriott, F. H. C. Separating mixtures of normal distributions. Biometrics 31 (1975) 767-769. McLachlan, G. J. The classification and mixture maximum likelihood approaches to cluster analysis.

On the interface between cluster analysis, principal component analysis, and multidimensional scaling. , Gupta, A. K. ): Multivariate statistical modeling and data analysis. Reidel, Dordrecht, 1987, 17-34. Bock, H. H. ) Classification and related methods of data analysis. Proc. First Conference of the International Federation of Classification Societies (IFCS-87), June 29 - July 1, 1987, Aachen/FRG. North Holland, Amsterdam, 1988. Boswell, St. B. Nonparametric estimation of the modes of high-dimensional densities.

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