| Analysis of Symbolic Data | |
| Dependencies and Variation Components of Symbolic Interval-Valued Data | p. 3 |
| On the Analysis of Symbolic Data | p. 13 |
| Symbolic Analysis to Learn Evolving CyberTraffic | p. 23 |
| A Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Hausdorff Distance | p. 35 |
| An Agglomerative Hierarchical Clustering Algorithm for Improving Symbolic Object Retrieval | p. 45 |
| 3WaySym-Scal: Three-Way Symbolic Multidimensional Scaling | p. 55 |
| Clustering and Validation of Interval Data | p. 69 |
| Building Symbolic Objects from Data Streams | p. 83 |
| Feature Clustering Method to Detect Monotonic Chain Structures in Symbolic Data | p. 95 |
| Symbolic Markov Chains | p. 103 |
| Quality Issues in Symbolic Data Analysis | p. 113 |
| Dynamic Clustering of Histogram Data: Using the Right Metric | p. 123 |
| Clustering Methods | |
| Beyond the Pyramids: Rigid Clustering Systems | p. 137 |
| Indirect Blockmodeling of 3-Way Networks | p. 151 |
| Clustering Methods: A History of [kappa]-Means Algorithms | p. 161 |
| Overlapping Clustering in a Graph Using [kappa]-Means and Application to Protein Interactions Networks | p. 173 |
| Species Clustering via Classical and Interval Data Representation | p. 183 |
| Looking for High Density Zones in a Graph | p. 193 |
| Block Bernoulli Parsimonious Clustering Models | p. 203 |
| Cluster Analysis Based on Posets | p. 213 |
| Hybrid [kappa]-Means: Combining Regression-Wise and Centroid-Based Criteria for QSAR | p. 225 |
| Partitioning by Particle Swarm Optimization | p. 235 |
| Conceptual Analysis of Data | |
| Concepts of a Discrete Random Variable | p. 247 |
| Mining Description Logics Concepts with Relational Concept Analysis | p. 259 |
| Representation of Concept Description by Multivalued Taxonomic Preordonance Variables | p. 271 |
| Recent Advances in Conceptual Clustering: Cluster3 | p. 285 |
| Symbolic Dynamics in Text: Application to Automated Construction of Concept Hierarchies | p. 299 |
| Consensus Methods | |
| Average Consensus and Infinite Norm Consensus: Two Methods for Ultrametric Trees | p. 309 |
| Consensus from Frequent Groupings | p. 317 |
| Consensus of Star Tree Hypergraphs | p. 325 |
| Data Analysis, Data Mining, and KDD | |
| Knowledge Management in Environmental Sciences with IKBS: Application to Systematics of Corals of the Mascarene Archipelago | p. 333 |
| Unsupervised Learning Informational Limit in Case of Sparsely Described Examples | p. 345 |
| Data Analysis and Operations Research | p. 357 |
| Reduction of Redundant Rules in Statistical Implicative Analysis | p. 367 |
| Mining Personal Banking Data to Detect Fraud | p. 377 |
| Finding Rules in Data | p. 387 |
| Mining Biological Data Using Pyramids | p. 397 |
| Association Rules for Categorical and Tree Data | p. 409 |
| Induction Graphs for Data Mining | p. 419 |
| Dissimilarities: Structures and Indices | |
| Clustering of Molecules: Influence of the Similarity Measures | p. 433 |
| Group Average Representations in Euclidean Distance Cones | p. 445 |
| On Lower-Maximal Paired-Ultrametrics | p. 455 |
| A Note on Three-Way Dissimilarities and Their Relationship with Two-Way Dissimilarities | p. 465 |
| One-to-One Correspondence Between Indexed Cluster Structures and Weakly Indexed Closed Cluster Structures | p. 477 |
| Adaptive Dissimilarity Index for Gene Expression Profiles Classification | p. 483 |
| Lower (Anti-) Robinson Rank Representations for Symmetric Proximity Matrices | p. 495 |
| Density-Based Distances: a New Approach for Evaluating Proximities Between Objects. Applications in Clustering and Discriminant Analysis | p. 505 |
| Robinson Cubes | p. 515 |
| Multivariate Statistics | |
| Relative and Absolute Contributions to Aid Strata Interpretation | p. 527 |
| Classification and Generalized Principal Component Analysis | p. 539 |
| Locally Linear Regression and the Calibration Problem for Micro-Array Analysis | p. 549 |
| Sanskrit Manuscript Comparison for Critical Edition and Classification | p. 557 |
| Divided Switzerland | p. 567 |
| Prediction with Confidence | p. 577 |
| Which Bootstrap for Principal Axes Methods? | p. 581 |
| PCR and PLS for Clusterwise Regression on Functional Data | p. 589 |
| A New Method for Ranking n Statistical Units | p. 599 |
| About Relational Correlations | p. 609 |
| Dynamic Features Extraction in Soybean Futures Market of China | p. 619 |
| Index | p. 629 |
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