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Classification, Clustering, and Data Mining Applications

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Cover of 'Classification, Clustering, and Data Mining Applications'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Thinking Ultrametrically
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    Chapter 2 Clustering by Vertex Density in a Graph
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    Chapter 3 Clustering by Ant Colony Optimization
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    Chapter 4 A Dynamic Cluster Algorithm Based on L r Distances for Quantitative Data
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    Chapter 5 The Last Step of a New Divisive Monothetic Clustering Method: the Gluing-Back Criterion
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    Chapter 6 Standardizing Variables in K-means Clustering
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    Chapter 7 A Self-Organizing Map for Dissimilarity Data
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    Chapter 8 Another Version of the Block EM Algorithm
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    Chapter 9 Controlling the Level of Separation of Components in Monte Carlo Studies of Latent Class Models
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    Chapter 10 Fixing Parameters in the Constrained Hierarchical Classification Method: Application to Digital Image Segmentation
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    Chapter 11 New Approaches for Sum-of-Diameters Clustering
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    Chapter 12 Spatial Pyramidal Clustering Based on a Tessellation
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    Chapter 13 Relative Projection Pursuit and its Application
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    Chapter 14 Priors for Neural Networks
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    Chapter 15 Combining Models in Discrete Discriminant Analysis Through a Committee of Methods
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    Chapter 16 Phoneme Discrimination with Functional Multi-Layer Perceptrons
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    Chapter 17 PLS Approach for Clusterwise Linear Regression on Functional Data
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    Chapter 18 On Classification and Regression Trees for Multiple Responses
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    Chapter 19 Subsetting Kernel Regression Models Using Genetic Algorithm and the Information Measure of Complexity
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    Chapter 20 Cherry-Picking as a Robustness Tool
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    Chapter 21 Academic Obsessions and Classification Realities: Ignoring Practicalities in Supervised Classification
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    Chapter 22 Modified Biplots for Enhancing Two-Class Discriminant Analysis
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    Chapter 23 Weighted Likelihood Estimation of Person Locations in an Unfolding Model for Polytomous Responses
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    Chapter 24 Classification of Geospatial Lattice Data and their Graphical Representation
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    Chapter 25 Degenerate Expectation-Maximization Algorithm for Local Dimension Reduction
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    Chapter 26 A Dimension Reduction Technique for Local Linear Regression
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    Chapter 27 Reducing the Number of Variables Using Implicative Analysis
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    Chapter 28 Optimal Discretization of Quantitative Attributes for Association Rules
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    Chapter 29 Clustering Methods in Symbolic Data Analysis
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    Chapter 30 Dependencies in Bivariate Interval-Valued Symbolic Data
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    Chapter 31 Clustering of Symbolic Objects Described by Multi-Valued and Modal Variables
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    Chapter 32 A Hausdorff Distance Between Hyper-Rectangles for Clustering Interval Data
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    Chapter 33 Kolmogorov-Smirnov for Decision Trees on Interval and Histogram Variables
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    Chapter 34 Dynamic Cluster Methods for Interval Data Based on Mahalanobis Distances
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    Chapter 35 A Symbolic Model-Based Approach for Making Collaborative Group Recommendations
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    Chapter 36 Probabilistic Allocation of Aggregated Statistical Units in Classification Trees for Symbolic Class Description
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    Chapter 37 Building Small Scale Models of Multi-Entity Databases By Clustering
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    Chapter 38 Phylogenetic Closure Operations and Homoplasy-Free Evolution
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    Chapter 39 Consensus of Classification Systems, with Adams’ Results Revisited
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    Chapter 40 Symbolic Linear Regression with Taxonomies
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    Chapter 41 Determining Horizontal Gene Transfers in Species Classification: Unique Scenario
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    Chapter 42 Active and Passive Learning to Explore a Complex Metabolism Data Set
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    Chapter 43 Mathematical and Statistical Modeling of Acute Inflammation
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    Chapter 44 Combining Functional MRI Data on Multiple Subjects
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    Chapter 45 Classifying the State of Parkinsonism by Using Electronic Force Platform Measures of Balance
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    Chapter 46 Subject Filtering for Passive Biometric Monitoring
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    Chapter 47 Mining Massive Text Data and Developing Tracking Statistics
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    Chapter 48 Contributions of Textual Data Analysis to Text Retrieval
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    Chapter 49 Automated Resolution of Noisy Bibliographic References
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    Chapter 50 Choosing the Right Bigrams for Information Retrieval
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    Chapter 51 A Mixture Clustering Model for Pseudo Feedback in Information Retrieval
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    Chapter 52 Analysis of Cross-Language Open-Ended Questions Through MFACT
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    Chapter 53 Inferring User’s Information Context from User Profiles and Concept Hierarchies
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    Chapter 54 Database Selection for Longer Queries
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    Chapter 55 An Overview of Collapsibility
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    Chapter 56 Generalized Factor Analyses for Contingency Tables
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    Chapter 57 A PLS Approach to Multiple Table Analysis
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    Chapter 58 Simultaneous Row and Column Partitioning in Several Contingency Tables
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    Chapter 59 Missing Data and Imputation Methods in Partition of Variables
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    Chapter 60 The Treatment of Missing Values and its Effect on Classifier Accuracy
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    Chapter 61 Clustering with Missing Values: No Imputation Required
Overall attention for this book and its chapters
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Citations

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Title
Classification, Clustering, and Data Mining Applications
Published by
Springer Berlin Heidelberg, January 2004
DOI 10.1007/978-3-642-17103-1
ISBNs
978-3-54-022014-5, 978-3-64-217103-1
Editors

Dr. David Banks, Dr. Frederick R. McMorris, Dr. Phipps Arabie, Prof. Dr. Wolfgang Gaul

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Poland 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Researcher 9 16%
Student > Master 8 14%
Student > Bachelor 6 11%
Lecturer 2 4%
Other 7 12%
Unknown 13 23%
Readers by discipline Count As %
Computer Science 18 32%
Engineering 7 12%
Mathematics 4 7%
Agricultural and Biological Sciences 2 4%
Economics, Econometrics and Finance 2 4%
Other 10 18%
Unknown 14 25%

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