Data Mining and Knowledge Discovery Handbook
Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a l...
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| Other Authors: | , |
| Format: | Electronic |
| Language: | English |
| Published: |
Boston, MA :
Springer US,
2010.
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| Edition: | 2. |
| Subjects: | |
| Online Access: | https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-09823-4 |
Table of Contents:
- New Added Topics: Graph Mining
- Sequence Mining
- Utility-Based Data Mining
- Swarm Intelligence.-Privacy Preserving DM
- Multimedia Data Mining
- Data Streaming Mining
- Data Mining in Bioinformatics
- Ontology Mining
- Reliability Issues of Knowledge Discovery
- Optimization-based Data Mining
- Distributed Data Mining
- Standards for Data Mining
- The Clementine Software
- The SAS Miner. All other topics updated to cover developments in the field: Introduction to knowledge discovery in databases
- Part I Preprocessing methods
- Data cleansing
- Handling missing attribute values
- Geometric methods for feature extraction and dimensional reduction
- Dimension Reduction and feature selection
- Discretization methods
- outlier detection
- Part II Supervised methods
- Introduction to supervised methods
- Decision trees
- Bayesian networks
- Data mining within a regression framework
- Support vector machines
- Part III Unsupervised methods
- Clustering methods
- Association rules
- Frequent set mining
- Constraint-based data mining
- Link analysis
- Part IV Soft computing methods
- Evolutionary algorithms for data mining
- Reinforcement-learning: an overview from a data mining perspective
- Neural networks
- Granular computing and rough sets
- Part V Supporting methods
- Statistical methods for data mining
- Logics for data mining
- Wavelet methods in data mining
- Fractal mining
- Interestingness measures
- Quality assessment approaches in data mining
- Data mining model comparison
- Data mining query languages
- Part VI Advanced methods
- Meta-learning
- Bias vs variance decomposition for regression and classification
- Mining with rare cases
- Mining data streams
- Mining high-dimensional data
- Text mining and information extraction
- Spatial data mining
- Data mining for imbalanced datasets: an overview
- Relational data mining
- Web mining
- A review of web document clustering approaches
- Causal discovery
- Ensemble methods for classifiers
- Decomposition methodology for knowledge discovery and data mining
- Information fusion
- Parallel and grid-based data mining
- Collaborative data mining
- Organizational data mining
- Mining time series data
- Part VII Applications
- Data mining in medicine
- Learning information patterns in biological databases
- Data mining for selection of manufacturing processes
- Data mining in telecommunications
- Data mining for financial applications
- Data mining for intrusion detection
- Data mining for software testing
- Data mining for CRM
- Data mining for target marketing
- Part VIII Software
- GainSmarts data mining system for marketing
- Index.