Martin Schader

New Approaches in Classification and Data Analysis

Classification and Clustering: Problems for the Future.- From classifications to cognitive categorization: the example of the road lexicon.- A review of graphical methods in Japan-from histogram to dynamic display.- New Data and New Tools: A Hypermedia Environment for Navigating Statistical Knowledge in Data Science.- On the logical necessity and priority of a monothetic conception of class, and on the consequent inadequacy of polythetic accounts of category and categorization.- Research and Applications of Quantification Methods in East Asian Countries.- Algorithms for a geometrical P.C.A. with the L1-norm.- Comparison of hierarchical classifications.- On quadripolar Robinson dissimilarity matrices.- An Ordered Set Approach to Neutral Consensus Functions.- From Apresjan Hierarchies and Bandelt-Dress Weak hierarchies to Quasi-hierarchies.- Spanning trees and average linkage clustering.- Adjustments of tree metrics based on minimum spanning trees.- The complexity of the median procedure for binary trees.- A multivariate analysis of a series of variety trials with special reference to classification of varieties.- Quality control of mixture. Application: The grass.- Mixture Analysis with Noisy Data.- Locally optimal tests on spatial clustering.- Choosing the Number of Clusters, Subset Selection of Variables, and Outlier Detection in the Standard Mixture-Model Cluster Analysis.- An examination of procedures for determining the number of clusters in a data set.- The gap test: an optimal method for determining the number of natural classes in cluster analysis.- Mode detection and valley seeking by binary morphological analysis of connectivity for pattern classification.- Interactive Class Classification Using Types.- K-means clustering in a low-dimensional Euclidean space.- Complexity relaxation of dynamic programming for cluster analysis.- Partitioning Problems in Cluster Analysis: A Review of Mathematical Programming Approaches.- Clusters and factors: neural algorithms for a novel representation of huge and highly multidimensional data sets.- Graphs and structural similarities.- A generalisation of the diameter criterion for clustering.- Percolation and multimodal data structuring.- Classification and Discrimination Techniques Applied to the Early Detection of Business Failure.- Recursive Partition and Symbolic Data Analysis.- Interpretation Tools For Generalized Discriminant Analysis.- Inference about rejected cases in discriminant analysis.- Structure Learning of Bayesian Networks by Genetic Algorithms.- On the representation of observational data used for classification and identification of natural objects.- Alternative strategies and CATANOVA testing in two-stage binary segmentation.- Alignment, Comparison and Consensus of Molecular Sequences.- An Empirical Evaluation of Consensus Rules for Molecular Sequences.- A Probabilistic Approach To Identifying Consensus In Molecular Sequences.- Applications of Distance Geometry to Molecular Conformation.- Classification of aligned biological sequences.- Use of Pyramids in Symbolic Data Analysis.- Proximity Coefficients between Boolean symbolic objects.- Conceptual Clustering in Structured Domains: A Theory Guided Approach.- Automatic Aid to Symbolic Cluster Interpretation.- Symbolic Clustering Algorithms using Similarity and Dissimilarity Measures.- Feature Selection for Symbolic Data Classification.- Towards extraction method of knowledge founded by symbolic objects.- One Method of Classification based on an Analysis of the Structural Relationship between Independent Variables.- The Integration of Neural Networks with Symbolic Knowledge Processing.- Ordering of Fuzzy k-Partitions.- On the Extension of Probability Theory and Statistics to the Handling of Fuzzy Data.- Fuzzy Regression.- Clustering and Aggregation of Fuzzy Preference Data: Agreement vs. Information.- Rough Classification with Valued Closeness Relation.- Representing proximities by network models.- An Eigenvector Algorithm to Fit lp-Distance Matrices.- A non linear approach to Non Symmetrical Data Analysis.- An Algorithmic Approach to Bilinear Models for Two-Way Contingency Tables.- New Approaches Based on Rankings in Sensory Evaluation.- Estimating failure times distributions from censored systems arranged in series.- Calibration Used as a Nonresponse Adjustment.- Least Squares Smoothers and Additive Decomposition.- High Dimensional Representations and Information Retrieval.- Experiments of Textual Data Analysis at Electricite de France.- Conception of a Data Supervisor in the Prospect of Piloting Management Quality of Service and Marketing.- Discriminant Analysis Using Textual Data.- Recent Developments in Case Based Reasoning: Improvements of Similarity Measures.- Contiguity in discriminant factorial analysis for image clustering.- Exploratory and Confirmatory Discrete Multivariate Analysis in a Probabilistic Approach for Studying the Regional Distribution of Aids in Angola.- Factor Analysis of Medical Image Sequences (FAMIS): Fundamental principles and applications.- Multifractal Segmentation of Medical Images.- The Human Organism-a Place to Thrive for the Immuno-Deficiency Virus.- Comparability and usefulness of newer and classical data analysis techniques. Application in medical domain classification.- The Classification of IRAS Point Sources.- Astronomical classification of the Hipparcos input catalogue.- Group identification and individual assignation of stars from kinematical and luminosity parameters.- Specific numerical and symbolic analysis of chronological series in view to classification of long period variable stars.- Author and Subject Index.

Data analysis, classification, and related methods

The volume presents new developments in data analysis and classification, and gives a state of the art impression of these scientific fields at the turn of the Millenium. Areas that receive considerable attention in this book are Cluster Analysis, Data Mining, Multidimensional and Symbolic Data Analysis, Decision and Regression Trees. The volume contains a refereed selection of original papers, overview papers, and innovative applications presented at the 7th Conference of the International Federation of Classification Societies (IFCS-2000), with contributions from eminent scientists all over the world. The reader finds introductory material into various areas and kaleidoscopic views of recent technical and methodological developments in widely different areas within data analysis and classification. The presence of a large number of application papers demonstrates the usefulness of the recently developed techniques.

Between Data Science and Applied Data Analysis

The volume presents new developments in data analysis and classification and gives an overview of the state of the art in these scientific fields and relevant applications. Areas that receive considerable attention in the book are clustering, discriminitation, data analysis, and statistics, as well as applications in economics, biology, and medicine. The reader will find material on recent technical and methodological developments and a large number of application papers demonstrating the usefulness of the newly developed techniques.

A New Algorithm for Two-Mode Clustering

Against the background that one-mode clustering, which is based on similarity or dissimilarity data, is well known and widely used, quite a number of generalizations of the basic clustering methodology have been developed. For so-called two-mode data we report on research within the area of two-mode clustering and describe the new AE (Alternating Exchanges) algorithm. For non- overlapping two-mode clustering this algorithm is based on the exchange of the cluster membership of elements from the sets of different modes. Therefore, it is simple and very fast. The results of applying this algorithm to several concrete data sets are compared to those of an own PENCLUS (PENalty CLUStering) approach.

Two-Mode Overlapping Clustering With Applications to Simultaneous Benefit Segmentation and Market Structuring

A new two-mode overlapping clustering procedure is presented. This procedure includes solution possibilities for two-mode (non-)overlapping additive clustering as well as (non-)overlapping clusterwise regression with conjoint experiments and can be used for simultaneous benefit segmentation and market structuring. Applications of various cases of the new procedure to conjoint data are used for comparisons.

Two Rules of Thumb for the Approximation of the Binomial Distribution by the Normal Distribution

In this department The American Statistician publishes articles, reviews, under the section heading. Articles and notes for the department, but not and notes of interest to teachers of the first mathematical statistics course intended specifically for the section, should be useful to a substantial and of applied statistics courses. The department includes the Accent on number of teachers of the indicated types of courses or should have the Teaching Materials section; suitable contents for the section are described potential for fundamentally affecting the way in which a course is taught.

Fitting Parametric Lorenz Curves to Grouped Income Distributions--A Critical Note

The paper surveys various parametric Lorenz curves to be fitted to grouped income data in order to obtain an estimate for the Gini measure of inequality. The curves are fitted to 16 sets of empirical income data. The results are compared to the results of the purely nonparametric method (due to Gastwirth) of computing lower and upper bounds for the Gini measure. It is shown that most of the parametric curves are unreliable in that they may produce estimates outside the bounds.

Architecture for the development of context-sensitive mobile applications

Recent advances in the development of mobile terminals and the appropriate communication infrastructures have the consequence that new kinds of applications arise. This trend leads to more and more complex mobile client applications as well. In this paper, we present a generic architecture, which can be used for the development of context-sensitive mobile applications.

Interoperability and interchangeability of middleware components in a three-tier CORBA-environment-state of the art

The paper covers heterogeneous multi-tier environments based on CORBA and Java. On the client side there is a Web browser that connects to the middle tier through CORBA-based Java applets. The business logic is decoupled from the database server on the third tier. Access to the databases is made through a JDBC interface or with embedded SQL. We present considerations about the use of different browsers and multiple ORBs, concentrating on interoperability issues. We explore the problems that have to be faced and give practical advice on how to make things work.

Context-aware User Interface Framework for Mobile Applications

Mobile devices available today are very heterogenous with regard to their display and input capabilities and used software platform configurations. This leads to a complex development process for applications which have to target a wide range of devices. Adapting the interface of an application to the current context of the user adds another burden to developers, while improving the user experience. Therefore we developed an XUL-based User Interface Framework which eases the development of mobile applications by separating the UI adaption from the application logic and offering portability to different Java ME platform configurations. Using this framework the user interface (UI) adapts itself automatically on context-changes and changes to different screen resolutions or orientations without increasing code complexity for the developers.