Data mining : foundations and intelligent paradigms Volume 1, Clustering, association and classification
Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 1of this three volume series, we have brought together contributions from some of the most prestigious researchers in the fundamental data mining tasks of clustering, association and classific...
Enregistré dans:
| Autres auteurs: | , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Collection: | Intelligent Systems Reference Library
23 |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Data Mining: Foundations and Intelligent Paradigms, Texte imprimé, 9783642231650 |
Table des matières:
- Introductory Chapter Clustering Analysis in Large Graphs with Rich Attributes Temporal Data Mining: Similarity-Profiled Association Pattern Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets Randomized Algorithm of Finding the True Number of Clusters Based on Chebychev Polynomial Approximation Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters DepMiner: A method and a system for the extraction of significant dependencies Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries Text Clustering with Named Entities: A Model, Experimentation and Realization Regional Association Rule Mining and Scoping from Spatial Data Learning from Imbalanced Data: Evaluation Matters