Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)

Qin, Zengchang, Tang, Yongchuan

ISBN 10: 3642412505 / ISBN 13: 9783642412509

Editore: Springer, 2014

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Codice articolo ZB1314411

Dati bibliografici

• Titolo: Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)
• Editore: Springer
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 3642412505
• ISBN 13: 9783642412509
• Rilegatura: Rilegato
• Condizione: Fine

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Riassunto

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.