Author Description

<p>Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. <i>Uncertainty Modeling for Data Mining: A Label Semantics Approach</i> 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.</p><p><b>Zengchang Qin</b> is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; <b>Yongchuan Tang</b> is an associate professor at the College of Computer Science, Zhejiang University, China.</p>