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

AvDavid Banks,Leanna House

Häftad, Engelska, 2004

Del i serien Studies in Classification, Data Analysis, and Knowledge Organization

1 632 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2004-06-09
Mått:155 x 235 x 37 mm
Vikt:1 007 g
Format:Häftad
Språk:Engelska
Serie:Studies in Classification, Data Analysis, and Knowledge Organization
Antal sidor:658
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540220145

Utforska kategorier

Referensverk och tvärvetenskap inom Samhälle och politik

Innehållsförteckning

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.

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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

AvDavid Banks,Leanna House

Häftad, Engelska, 2004

Del i serien Studies in Classification, Data Analysis, and Knowledge Organization

1 632 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

Produktinformation

Utgivningsdatum:2004-06-09
Mått:155 x 235 x 37 mm
Vikt:1 007 g
Format:Häftad
Språk:Engelska
Serie:Studies in Classification, Data Analysis, and Knowledge Organization
Antal sidor:658
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540220145

Utforska kategorier

Referensverk och tvärvetenskap inom Samhälle och politik

Innehållsförteckning

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.