Ebook: 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

Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

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Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

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Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.
Content:
Front Matter....Pages I-XIV
Front Matter....Pages 1-1
Thinking Ultrametrically....Pages 3-14
Clustering by Vertex Density in a Graph....Pages 15-23
Clustering by Ant Colony Optimization....Pages 25-32
The Last Step of a New Divisive Monothetic Clustering Method: the Gluing-Back Criterion....Pages 33-41
Standardizing Variables in K-means Clustering....Pages 43-51
A Self-Organizing Map for Dissimilarity Data....Pages 53-60
Another Version of the Block EM Algorithm....Pages 61-68
Controlling the Level of Separation of Components in Monte Carlo Studies of Latent Class Models....Pages 69-76
Fixing Parameters in the Constrained Hierarchical Classification Method: Application to Digital Image Segmentation....Pages 77-84
New Approaches for Sum-of-Diameters Clustering....Pages 85-94
Spatial Pyramidal Clustering Based on a Tessellation....Pages 95-103
Front Matter....Pages 105-120
Relative Projection Pursuit and its Application....Pages 121-121
Priors for Neural Networks....Pages 123-139
Combining Models in Discrete Discriminant Analysis Through a Committee of Methods....Pages 141-150
Phoneme Discrimination with Functional Multi-Layer Perceptrons....Pages 151-156
PLS Approach for Clusterwise Linear Regression on Functional Data....Pages 157-165
On Classification and Regression Trees for Multiple Responses....Pages 167-176
Subsetting Kernel Regression Models Using Genetic Algorithm and the Information Measure of Complexity....Pages 177-184
Cherry-Picking as a Robustness Tool....Pages 185-196
Front Matter....Pages 197-206
Academic Obsessions and Classification Realities: Ignoring Practicalities in Supervised Classification....Pages 207-207
Modified Biplots for Enhancing Two-Class Discriminant Analysis....Pages 209-232
Weighted Likelihood Estimation of Person Locations in an Unfolding Model for Polytomous Responses....Pages 233-240
Classification of Geospatial Lattice Data and their Graphical Representation....Pages 241-249
Degenerate Expectation-Maximization Algorithm for Local Dimension Reduction....Pages 251-258
A Dimension Reduction Technique for Local Linear Regression....Pages 259-268
Reducing the Number of Variables Using Implicative Analysis....Pages 269-276
Optimal Discretization of Quantitative Attributes for Association Rules....Pages 277-285
Front Matter....Pages 287-296
Clustering Methods in Symbolic Data Analysis....Pages 297-297
Dependencies in Bivariate Interval-Valued Symbolic Data....Pages 299-317
Clustering of Symbolic Objects Described by Multi-Valued and Modal Variables....Pages 319-324
A Hausdorff Distance Between Hyper-Rectangles for Clustering Interval Data....Pages 325-332
Kolmogorov-Smirnov for Decision Trees on Interval and Histogram Variables....Pages 333-339
Dynamic Cluster Methods for Interval Data Based on Mahalanobis Distances....Pages 341-350
A Symbolic Model-Based Approach for Making Collaborative Group Recommendations....Pages 351-360
Probabilistic Allocation of Aggregated Statistical Units in Classification Trees for Symbolic Class Description....Pages 361-369
Building Small Scale Models of Multi-Entity Databases By Clustering....Pages 371-379
Front Matter....Pages 381-391
Phylogenetic Closure Operations and Homoplasy-Free Evolution....Pages 393-393
Consensus of Classification Systems, with Adams’ Results Revisited....Pages 395-416
Symbolic Linear Regression with Taxonomies....Pages 417-428
Front Matter....Pages 429-437
Determining Horizontal Gene Transfers in Species Classification: Unique Scenario....Pages 393-393
Active and Passive Learning to Explore a Complex Metabolism Data Set....Pages 439-446
Mathematical and Statistical Modeling of Acute Inflammation....Pages 447-456
Combining Functional MRI Data on Multiple Subjects....Pages 457-467
Classifying the State of Parkinsonism by Using Electronic Force Platform Measures of Balance....Pages 469-476
Subject Filtering for Passive Biometric Monitoring....Pages 477-483
Front Matter....Pages 485-492
Mining Massive Text Data and Developing Tracking Statistics....Pages 493-493
Contributions of Textual Data Analysis to Text Retrieval....Pages 495-510
Automated Resolution of Noisy Bibliographic References....Pages 511-520
Choosing the Right Bigrams for Information Retrieval....Pages 521-530
A Mixture Clustering Model for Pseudo Feedback in Information Retrieval....Pages 531-540
Analysis of Cross-Language Open-Ended Questions Through MFACT....Pages 541-551
Inferring User’s Information Context from User Profiles and Concept Hierarchies....Pages 553-561
Database Selection for Longer Queries....Pages 563-573
Front Matter....Pages 575-584
An Overview of Collapsibility....Pages 585-585
Generalized Factor Analyses for Contingency Tables....Pages 587-596
A PLS Approach to Multiple Table Analysis....Pages 597-606
Simultaneous Row and Column Partitioning in Several Contingency Tables....Pages 607-620
Missing Data and Imputation Methods in Partition of Variables....Pages 621-629
The Treatment of Missing Values and its Effect on Classifier Accuracy....Pages 631-637
Clustering with Missing Values: No Imputation Required....Pages 639-647
....Pages 649-658