Data mining : foundations and practice
This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigm...
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| Hlavní autor: | |
|---|---|
| Další autoři: | , , , , , , , |
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2008. |
| Edice: | Studies in Computational Intelligence
118 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
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 Practice, Texte imprimé, 9783540784876 • Data Mining: Foundations and Practice, Texte imprimé, 9783540870975 • Data Mining: Foundations and Practice, Texte imprimé, 9783642097225 • Data Mining: Foundations and Practice, Texte imprimé, 9783540784876 |
Obsah:
- Compact Representations of Sequential Classification Rules An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions Mining Linguistic Trends from Time Series Latent Semantic Space for Web Clustering A Logical Framework for Template Creation and Information Extraction A Bipolar Interpretation of Fuzzy Decision Trees A Probability Theory Perspective on the Zadeh Fuzzy System Three Approaches to Missing Attribute Values: A Rough Set Perspective MLEM2 Rule Induction Algorithms: With and Without Merging Intervals Towards a Methodology for Data Mining Project Development: The Importance of Abstraction Fining Active Membership Functions in Fuzzy Data Mining A Compressed Vertical Binary Algorithm for Mining Frequent Patterns Naïve Rules Do Not Consider Underlying Causality Inexact Multiple-Grained Causal Complexes Does Relevance Matter to Data Mining Research? E-Action Rules Mining E-Action Rules, System DEAR Definability of Association Rules and Tables of Critical Frequencies Classes of Association Rules: An Overview Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types On the Complexity of the Privacy Problem in Databases Ensembles of Least Squares Classifiers with Randomized Kernels On Pseudo-Statistical Independence in a Contingency Table Role of Sample Size and Determinants in Granularity of Contingency Matrix Generating Concept Hierarchies from User Queries Mining Efficiently Significant Classification Association Rules Data Preprocessing and Data Mining as Generalization Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams A Conceptual Framework of Data Mining How to Prevent Private Data from being Disclosed to a Malicious Attacker Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method