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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