Classification, Clustering, and Data Mining Applications

I New Methods in Cluster Analysis.- Thinking Ultrametrically.- Clustering by Vertex Density in a Graph.- Clustering by Ant Colony Optimization.- A Dynamic Cluster Algorithm Based on Lr Distances for Quantitative Data.- The Last Step of a New Divisive Monothetic Clustering Method: the Gluing-Back Criterion.- Standardizing Variables in K-means Clustering.- A Self-Organizing Map for Dissimilarity Data.- Another Version of the Block EM Algorithm.- Controlling the Level of Separation of Components in Monte Carlo Studies of Latent Class Models.- Fixing Parameters in the Constrained Hierarchical Classification Method: Application to Digital Image Segmentation.- New Approaches for Sum-of-Diameters Clustering.- Spatial Pyramidal Clustering Based on a Tessellation.- II Modern Nonparametrics.- Relative Projection Pursuit and its Application.- Priors for Neural Networks.- Combining Models in Discrete Discriminant Analysis Through a Committee of Methods.- Phoneme Discrimination with Functional Multi-Layer Perceptrons.- PLS Approach for Clusterwise Linear Regression on Functional Data.- On Classification and Regression Trees for Multiple Responses.- Subsetting Kernel Regression Models Using Genetic Algorithm and the Information Measure of Complexity.- Cherry-Picking as a Robustness Tool.- III Classification and Dimension Reduction.- Academic Obsessions and Classification Realities: Ignoring Practicalities in Supervised Classification.- Modified Biplots for Enhancing Two-Class Discriminant Analysis.- Weighted Likelihood Estimation of Person Locations in an Unfolding Model for Polytomous Responses.- Classification of Geospatial Lattice Data and their Graphical Representation.- Degenerate Expectation-Maximization Algorithm for Local Dimension Reduction.- A Dimension Reduction Techniquefor Local Linear Regression.- Reducing the Number of Variables Using Implicative Analysis.- Optimal Discretization of Quantitative Attributes for Association Rules.- IV Symbolic Data Analysis.- Clustering Methods in Symbolic Data Analysis.- Dependencies in Bivariate Interval-Valued Symbolic Data.- Clustering of Symbolic Objects Described by Multi-Valued and Modal Variables.- A Hausdorff Distance Between Hyper-Rectangles for Clustering Interval Data.- Kolmogorov-Smirnov for Decision Trees on Interval and Histogram Variables.- Dynamic Cluster Methods for Interval Data Based on Mahalanobis Distances.- A Symbolic Model-Based Approach for Making Collaborative Group Recommendations.- Probabilistic Allocation of Aggregated Statistical Units in Classification Trees for Symbolic Class Description.- Building Small Scale Models of Multi-Entity Databases by Clustering.- V Taxonomy and Medicine.- Phylogenetic Closure Operations and Homoplasy-Free Evolution.- Consensus of Classification Systems, with Adams' Results Revisited.- Symbolic Linear Regression with Taxonomies.- Determining Horizontal Gene Transfers in Species Classification: Unique Scenario.- Active and Passive Learning to Explore a Complex Metabolism Data Set.- Mathematical and Statistical Modeling of Acute Inflammation.- Combining Functional MRI Data on Multiple Subjects.- Classifying the State of Parkinsonism by Using Electronic Force Platform Measures of Balance.- Subject Filtering for Passive Biometric Monitoring.- VI Text Mining.- Mining Massive Text Data and Developing Tracking Statistics.- Contributions of Textual Data Analysis to Text Retrieval.- Automated Resolution of Noisy Bibliographic References.- Choosing the Right Bigrams for Information Retrieval.- A Mixture Clustering Model for Pseudo Feedback inInformation Retrieval.- Analysis of Cross-Language Open-Ended Questions Through MFACT.- Inferring User's Information Context from User Profiles and Concept Hierarchies.- Database Selection for Longer Queries.- VII Contingency Tables and Missing Data.- An Overview of Collapsibility.- Generalized Factor Analyses for Contingency Tables.- A PLS Approach to Multiple Table Analysis.- Simultaneous Rowand Column Partitioning in Several Contingency Tables.- Missing Data and Imputation Methods in Partition of Variables.- The Treatment of Missing Values and its Effect on Classifier Accuracy.- Clustering with Missing Values: No Imputation Required.