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.

机器学习和数据挖掘与不确定性有着密不可分的联系。用于学习的可观察数据通常是不精确的、不完整的或有噪音的。数据挖掘的不确定性建模。标签语义方法介绍了 "标签语义"，一种基于模糊逻辑的不确定性建模理论。提出了几种基于标签语义的新的数据挖掘算法，并在真实世界的数据集上进行了测试。还讨论了标签语义的原型解释和基于原型的新数据挖掘算法。本书为数据挖掘、模糊计算和不确定性推理领域的研究生、研究人员和其他专业人士提供了宝贵的资源。

秦增昌是北京航空航天大学自动化科学与电气工程学院的副教授；唐永川是浙江大学计算机学院的副教授。