Selected Contributions in Data Analysis and Classification

Table of Contents

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   Book Overview
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   Chapter 1 Dependencies and Variation Components of Symbolic Interval-Valued Data
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   Chapter 2 On the Analysis of Symbolic Data
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   Chapter 3 Symbolic Analysis to Learn Evolving CyberTraffic
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   Chapter 4 A Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Hausdorff Distance
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   Chapter 5 An Agglomerative Hierarchical Clustering Algorithm for Improving Symbolic Object Retrieval
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   Chapter 6 3 WaySym-Scal : Three-Way Symbolic Multidimensional Scaling
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   Chapter 7 Clustering and Validation of Interval Data
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   Chapter 8 Building Symbolic Objects from Data Streams
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    Chapter 9 Feature Clustering Method to Detect Monotonic Chain Structures in Symbolic Data
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    Chapter 10 Symbolic Markov Chains
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    Chapter 11 Quality Issues in Symbolic Data Analysis
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    Chapter 12 Dynamic Clustering of Histogram Data: Using the Right Metric
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    Chapter 13 Beyond the Pyramids: Rigid Clustering Systems
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    Chapter 14 Indirect Blockmodeling of 3-Way Networks
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    Chapter 15 Clustering Methods: A History of k -Means Algorithms
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    Chapter 16 Overlapping Clustering in a Graph Using k -Means and Application to Protein Interactions Networks
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    Chapter 17 Species Clustering via Classical and Interval Data Representation
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    Chapter 18 Looking for High Density Zones in a Graph
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    Chapter 19 Block Bernoulli Parsimonious Clustering Models
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    Chapter 20 Cluster Analysis Based on Posets
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    Chapter 21 Hybrid k -Means: Combining Regression-Wise and Centroid-Based Criteria for QSAR
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    Chapter 22 Partitioning by Particle Swarm Optimization
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    Chapter 23 Concepts of a Discrete Random Variable
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    Chapter 24 Mining Description Logics Concepts with Relational Concept Analysis
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    Chapter 25 Representation of Concept Description by Multivalued Taxonomic Preordonance Variables
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    Chapter 26 Recent Advances in Conceptual Clustering: CLUSTER3
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    Chapter 27 Symbolic Dynamics in Text: Application to Automated Construction of Concept Hierarchies
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    Chapter 28 Average Consensus and Infinite Norm Consensus : Two Methods for Ultrametric Trees
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    Chapter 29 Consensus from Frequent Groupings
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    Chapter 30 Consensus of Star Tree Hypergraphs
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    Chapter 31 Knowledge Management in Environmental Sciences with $$ \mathcal{I}\mathcal{K}\mathcal{B}\mathcal{S}: $$ : Application to Systematics of Corals of the Mascarene Archipelago
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    Chapter 32 Unsupervised Learning Informational Limit in Case of Sparsely Described Examples
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    Chapter 33 Data Analysis and Operations Research
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    Chapter 34 Reduction of Redundant Rules in Statistical Implicative Analysis
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    Chapter 35 Mining Personal Banking Data to Detect Fraud
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    Chapter 36 Finding Rules in Data
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    Chapter 37 Mining Biological Data Using Pyramids
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    Chapter 38 Association Rules for Categorical and Tree Data
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    Chapter 39 Induction Graphs for Data Mining
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    Chapter 40 Clustering of Molecules: Influence of the Similarity Measures
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    Chapter 41 Group Average Representations in Euclidean Distance Cones
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    Chapter 42 On Lower-Maximal Paired-Ultrametrics
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    Chapter 43 A Note on Three-Way Dissimilarities and Their Relationship with Two-Way Dissimilarities
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    Chapter 44 One-to-One Correspondence Between Indexed Cluster Structures and Weakly Indexed Closed Cluster Structures
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    Chapter 45 Adaptive Dissimilarity Index for Gene Expression Profiles Classification
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    Chapter 46 Lower (Anti-)Robinson Rank Representations for Symmetric Proximity Matrices
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    Chapter 47 Density-Based Distances: a New Approach for Evaluating Proximities Between Objects. Applications in Clustering and Discriminant Analysis
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    Chapter 48 Robinson Cubes
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    Chapter 49 Relative and Absolute Contributions to Aid Strata Interpretation
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    Chapter 50 Classification and Generalized Principal Component Analysis
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    Chapter 51 Locally Linear Regression and the Calibration Problem for Micro-Array Analysis
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    Chapter 52 Sanskrit Manuscript Comparison for Critical Edition and Classification
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    Chapter 53 Divided Switzerland
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    Chapter 54 Prediction with Confidence
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    Chapter 55 Which Bootstrap for Principal Axes Methods?
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    Chapter 56 PCR and PLS for Clusterwise Regression on Functional Data
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    Chapter 57 A New Method for Ranking n Statistical Units
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    Chapter 58 About Relational Correlations
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    Chapter 59 Dynamic Features Extraction in Soybean Futures Market of China