Series in Machine Perception and Artificial Intelligence - Vol. 61
DECOMPOSITION METHODOLOGY FOR KNOWLEDGE DISCOVERY AND DATA MINING
Theory and Applications
by Oded Maimon & Lior Rokach
Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery in Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools. The various elements are then joined together to solve the initial problem.
The benefits of decomposition methodology in data mining include: increased performance (classification accuracy); conceptual simplification of the problem; enhanced feasibility for huge databases; clearer and more comprehensible results; reduced runtime by solving smaller problems and by using parallel/distributed computation; and the opportunity of using different techniques for individual sub-problems.
- Knowledge Discovery and Data Mining: Concepts and
- Decision Trees
- Clustering Methods
- Ensemble Methods
- Elementary Decomposition Framework
- Feature Set Decomposition
- Space Decomposition
- Sample Decomposition
- Function Decomposition
- Concept Aggregation
- A Meta-Classification for Decomposition Methodology
Readership: Students and practitioners in information systems,
engineering, computer science, statistics and management.