TY  - BOOK
AU  - Qin,Zengchang
AU  - Tang,Yongchuan
ED  - SpringerLink (Online service)
TI  - Uncertainty Modeling for Data Mining: A Label Semantics Approach 
T2  - Advanced Topics in Science and Technology in China,
SN  - 9783642412516
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2014///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg, Imprint: Springer
KW  - Computer science
KW  - Information systems
KW  - Data mining
KW  - Artificial intelligence
KW  - Computer Science
KW  - Data Mining and Knowledge Discovery
KW  - Artificial Intelligence (incl. Robotics)
KW  - Information Systems and Communication Service
KW  - Math Applications in Computer Science
N2  - Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.   Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China
UR  - http://dx.doi.org/10.1007/978-3-642-41251-6
ER  -