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 - David Banks

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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

By: David Banks (Editor), Leanna House (Editor), Frederick R. McMorris (Editor)

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Paperback | 9 June 2004

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676 Pages

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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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New Methods in Cluster Analysis
Thinking Ultrametrically	p. 3
Clustering by Vertex Density in a Graph	p. 15
Clustering by Ant Colony Optimization	p. 25
A Dynamic Cluster Algorithm Based on L_r Distances for Quantitative Data	p. 33
The Last Step of a New Divisive Monothetic Clustering Method: the Gluing-Back Criterion	p. 43
Standardizing Variables in K-means Clustering	p. 53
A Self-Organizing Map for Dissimilarity Data	p. 61
Another Version of the Block EM Algorithm	p. 69
Controlling the Level of Separation of Components in Monte Carlo Studies of Latent Class Models	p. 77
Fixing Parameters in the Constrained Hierarchical Classification Method: Application to Digital Image Segmentation	p. 85
New Approaches for Sum-of-Diameters Clustering	p. 95
Spatial Pyramidal Clustering Based on a Tessellation	p. 105
Modern Nonparametrics
Relative Projection Pursuit and its Application	p. 123
Priors for Neural Networks	p. 141
Combining Models in Discrete Discriminant Analysis Through a Committee of Methods	p. 151
Phoneme Discrimination with Functional Multi-Layer Perceptrons	p. 157
PLS Approach for Clusterwise Linear Regression on Functional Data	p. 167
On Classification and Regression Trees for Multiple Responses	p. 177
Subsetting Kernel Regression Models Using Genetic Algorithm and the Information Measure of Complexity	p. 185
Cherry-Picking as a Robustness Tool	p. 197
Classification and Dimension Reduction
Academic Obsessions and Classification Realities: Ignoring Practicalities in Supervised Classification	p. 209
Modified Biplots for Enhancing Two-Class Discriminant Analysis	p. 233
Weighted Likelihood Estimation of Person Locations in an Unfolding Model for Polytomous Responses	p. 241
Classification of Geospatial Lattice Data and their Graphical Representation	p. 251
Degenerate Expectation-Maximization Algorithm for Local Dimension Reduction	p. 259
A Dimension Reduction Technique for Local Linear Regression	p. 269
Reducing the Number of Variables Using Implicative Analysis	p. 277
Optimal Discretization of Quantitative Attributes for Association Rules	p. 287
Symbolic Data Analysis
Clustering Methods in Symbolic Data Analysis	p. 299
Dependencies in Bivariate Interval-Valued Symbolic Data	p. 319
Clustering of Symbolic Objects Described by Multi-Valued and Modal Variables	p. 325
A Hausdorff Distance Between Hyper-Rectangles for Clustering Interval Data	p. 333
Kolmogorov-Smirnov for Decision Trees on Interval and Histogram Variables	p. 341
Dynamic Cluster Methods for Interval Data Based on Mahalanobis Distances	p. 351
A Symbolic Model-Based Approach for Making Collaborative Group Recommendations	p. 361
Probabilistic Allocation of Aggregated Statistical Units in Classification Trees for Symbolic Class Description	p. 371
Building Small Scale Models of Multi-Entity Databases by Clustering	p. 381
Taxonomy and Medicine
Phylogenetic Closure Operations and Homoplasy-Free Evolution	p. 395
Consensus of Classification Systems, with Adams' Results Revisited	p. 417
Symbolic Linear Regression with Taxonomies	p. 429
Determining Horizontal Gene Transfers in Species Classification: Unique Scenario	p. 439
Active and Passive Learning to Explore a Complex Metabolism Data Set	p. 447
Mathematical and Statistical Modeling of Acute Inflammation	p. 457
Combining Functional MRI Data on Multiple Subjects	p. 469
Classifying the State of Parkinsonism by Using Electronic Force Platform Measures of Balance	p. 477
Subject Filtering for Passive Biometrie Monitoring	p. 485
Text Mining
Mining Massive Text Data and Developing Tracking Statistics	p. 495
Contributions of Textual Data Analysis to Text Retrieval	p. 511
Automated Resolution of Noisy Bibliographic References	p. 521
Choosing the Right Bigrams for Information Retrieval	p. 531
A Mixture Clustering Model for Pseudo Feedback in Information Retrieval	p. 541
Analysis of Cross-Language Open-Ended Questions Through MFACT	p. 553
Inferring User's Information Context from User Profiles and Concept Hierarchies	p. 563
Database Selection for Longer Queries	p. 575
Contingency Tables and Missing Data
An Overview of Collapsibility	p. 587
Generalized Factor Analyses for Contingency Tables	p. 597
A PLS Approach to Multiple Table Analysis	p. 607
Simultaneous Row and Column Partitioning in Several Contingency Tables	p. 621
Missing Data and Imputation Methods in Partition of Variables	p. 631
The Treatment of Missing Values and its Effect on Classifier Accuracy	p. 639
Clustering with Missing Values: No Imputation Required	p. 649
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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

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