Data Mining: Foundations and Practice /
| Autor Corporativo: | |
|---|---|
| Otros Autores: | , , , |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edición: | 1st ed. 2008. |
| Colección: | Studies in Computational Intelligence,
118 |
| Materias: |
Tabla de Contenidos:
- 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.