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.
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