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Hans-Hermann Bock

Bio: Hans-Hermann Bock is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Cluster analysis & Correlation clustering. The author has an hindex of 23, co-authored 71 publications receiving 3486 citations.


Papers
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Book
03 Feb 2000
TL;DR: This work focuses on Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective, and Symbolic Objects, where H.H. Bock and E. Diday focused on the former and the latter dealt with the latter.
Abstract: E. Diday: Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective.- H.H. Bock: The Classical Data Situation.- H.H. Bock: Symbolic Data.- H.H. Bock, E. Diday: Symbolic Objects.- V. Stephan, G. Hebrail, Y. Lechevallier: Generation of Symbolic Objects from Relational Databases.- P. Bertrand, F. Goupil: Descriptive Statistics for Symbolic Data.- M. Noirhomme-Fraiture, M. Rouard: Visualizing and Editing Symbolic Objects.- Similarity and Dissimilarity: F. Esposito, D. Malerba, V. Tamma, H.H. Bock: Classical Resemblance Measures.- H.H. Bock: Dissimilarity Measures for Probability Distributions.- F. Esposito, D. Malerba, V. Tamma: Dissimilarity Measures for Symbolic Objects.- F. Esposito, D. Malerba, F. Lisi: Matching Symbolic Objects.- Symbolic Factor Analysis: H.H.Bock: Classical Principal Component Analysis.- A. Chouakria, P. Cazes, E. Diday: Symbolic Principal Component Analysis.- N.C. Lauro, F. Palumbo, R. Verde: Factorial Discriminant Analysis on Symbolic Objects.- Discrimination: Assigning Symbolic Objects to Classes: J. Rasson, S. Lissoir: Classical Methods of Discrimination.- J. Rasson, S. Lissoir: Symbolic Kernel Discriminant Analysis.- E. Perinel, Y. Lechevalier: Symbolic Discrimination Rules.- M. Bravo Llatas, J. Garcia-Santesmases: Segmentation Trees for Stratified Data.- Clustering Methods for Symbolic Objects: M. Chavent, H.H. Bock: Clustering Problem, Clustering Methods for Classical Data.- M. Chavent: Criterion-Based Divisive Clustering for Symbolic Data.- P. Brito: Hierarchical and Pyramidal Clustering with Complete Symbolic Objects.- G. Polaillon: Pyramidal Classification for Interval Data Using Galois Lattice Reduction.- M. Gettler-Summa, C. Pardoux: Symbolic Approaches for Three-way Data.-Illustrative Benchmark Analysis: R. Bisdorff: Introduction.- R. Bisdorff: Professional Careers of Retired Working Persons.- A. Iztueta, P. Calvo: Labour Force Survey.- F. Goupil, M. Touati, E. Diday, R. Moult: Census Data from the Office for National Statistics.- A. Morineau: The SODAS Software Package.

605 citations

BookDOI
01 Jan 2000

441 citations

Journal ArticleDOI
TL;DR: This paper presents a structured overview of methods for two-mode clustering, that is, methods that provide a simultaneous clustering of the rows and columns of a rectangular data matrix.
Abstract: In this paper we present a structured overview of methods for two-mode clustering, that is, methods that provide a simultaneous clustering of the rows and columns of a rectangular data matrix. Key structuring principles include the nature of row, column and data clusters and the type of model structure or associated loss function. We illustrate with analyses of symptom data on archetypal psychiatric patients.

198 citations

Journal ArticleDOI
TL;DR: It is shown how suitable clustering criteria or grouping methods may be derived from probabilistic models for partition-type, hierarchical and tree-like clustering structures in the case of vector-valued data, dissimilarity matrices and similarity relations.

159 citations


Cited by
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01 Jan 1988

9,439 citations

Book
01 Jan 1988

8,586 citations

Journal ArticleDOI
Ali S. Hadi1
TL;DR: This book make understandable the cluster analysis is based notion of starsmodern treatment, which efficiently finds accurate clusters in data and discusses various types of study the user set explicitly but also proposes another.
Abstract: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase In both the increasingly important and distribution we show how these methods. Our experiments demonstrate that together can deal with most applications technometrics. In an appropriate visualization technique is to these new. The well written and efficiently finds accurate clusters in data including. Of applied value for several preprocessing tasks discontinuity preserving smoothing feature clusters! However the model based notion of domain knowledge from real data repositories in data. Discusses various types of study the user set explicitly but also propose another. This book make understandable the cluster analysis is based notion of starsmodern treatment.

7,423 citations

Journal ArticleDOI

6,278 citations

Journal ArticleDOI
TL;DR: The results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
Abstract: Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.

5,846 citations