Uncertainty modeling for data mining [electronic resource] : a label semantics approach / Zengchang Qin, Yongchuan Tang.

Uncertainty modeling for data mining [electronic resource] : a label semantics approach / Zengchang Qin, Yongchuan Tang.

9783642412516 electronic book
3642412513 electronic book
9783642412509
3642412505

Dordrecht : Springer, 2014.

English

1 online resource (420 pages) : illustrations.

10.1007/978-3-642-41251-6 doi

Q375

003/.54

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.

Includes bibliographical references.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed November 24, 2014).

Advanced topics in science and technology in China.

Print version: 9783642412509

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