Advances in Fuzzy Systems — Applications and Theory
NONLINEAR INTEGRALS AND THEIR APPLICATIONS IN DATA MINING
by Zhenyuan Wang (University of Nebraska at Omaha, USA), Rong Yang (Shen Zhen University, China) & Kwong-Sak Leung (Chinese University of Hong Kong, China)
Regarding the set of all feature attributes in a given database as the universal set, this monograph discusses various nonadditive set functions that describe the interaction among the contributions from feature attributes towards a considered target attribute. Then, the relevant nonlinear integrals are investigated. These integrals can be applied as aggregation tools in information fusion and data mining, such as synthetic evaluation, nonlinear multiregressions, and nonlinear classifications. Some methods of fuzzification are also introduced for nonlinear integrals such that fuzzy data can be treated and fuzzy information is retrievable.
The book is suitable as a text for graduate courses in mathematics, computer science, and information science. It is also useful to researchers in the relevant area.
Contents:
- Basic Knowledge on Classical Set Theory
- Fuzzy Sets
- Set Functions
-
Integrals
- Information Fusion
- Optimization and Soft Computing Techniques
- Identification of Signed Efficiency Measures
- Multiregressions Based on Nonlinear Integrals
- Classifications Based on Nonlinear Integrals
- Data Mining with Fuzzy Data
- Fuzzy Information Retrieval
- Applications in Bioinformatics
Readership: Graduate students and research students interested in mathematics
and computer science.
| 400pp (approx.) |
Pub. date: Scheduled Summer 2009 |
Request for inspection copy
|
