E-raamat: Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), Illinois Institute of Technology, Chicago, 15-18 July 2004

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 Users
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.