Mining Complex Data
| Autor Corporativo: | |
|---|---|
| Otros Autores: | , , , |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2009.
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| Edición: | 1st ed. 2009. |
| Colección: | Studies in Computational Intelligence,
165 |
| Materias: | |
| Acceso en línea: | https://doi.org/10.1007/978-3-540-88067-7 |
Tabla de Contenidos:
- General Aspects of Complex Data
- Using Layout Data for the Analysis of Scientific Literature
- Extracting a Fuzzy System by Using Genetic Algorithms for Imbalanced Datasets Classification: Application on Down’s Syndrome Detection
- A Hybrid Approach of Boosting Against Noisy Data
- Dealing with Missing Values in a Probabilistic Decision Tree during Classification
- Kernel-Based Algorithms and Visualization for Interval Data Mining
- Rules Extraction
- Evaluating Learning Algorithms Composed by a Constructive Meta-learning Scheme for a Rule Evaluation Support Method
- Mining Statistical Association Rules to Select the Most Relevant Medical Image Features
- From Sequence Mining to Multidimensional Sequence Mining
- Tree-Based Algorithms for Action Rules Discovery
- Graph Data Mining
- Indexing Structure for Graph-Structured Data
- Full Perfect Extension Pruning for Frequent Subgraph Mining
- Parallel Algorithm for Enumerating Maximal Cliques in Complex Network
- Community Finding of Scale-Free Network: Algorithm and Evaluation Criterion
- The k-Dense Method to Extract Communities from Complex Networks
- Data Clustering
- Efficient Clustering for Orders
- Exploring Validity Indices for Clustering Textual Data.