Uncertainty Modeling for Data Mining: A Label Semantics Approach

✍ Scribed by Prof. Zengchang Qin, Prof. Yongchuan Tang (auth.)

Publisher: Springer-Verlag Berlin Heidelberg
Year: 2014
Tongue: English
Leaves: 303
Series: Advanced Topics in Science and Technology in China
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-XIX
Introduction....Pages 1-12
Induction and Learning....Pages 13-38
Label Semantics Theory....Pages 39-75
Linguistic Decision Trees for Classification....Pages 77-119
Linguistic Decision Trees for Prediction....Pages 121-154
Bayesian Methods Based on Label Semantics....Pages 155-176
Unsupervised Learning with Label Semantics....Pages 177-192
Linguistic FOIL and Multiple Attribute Hierarchy for Decision Making....Pages 193-214
A Prototype Theory Interpretation of Label Semantics....Pages 215-233
Prototype Theory for Learning....Pages 235-252
Prototype-Based Rule Systems....Pages 253-275
Information Cells and Information Cell Mixture Models....Pages 277-291

✦ Subjects

Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Information Systems and Communication Service; Math Applications in Computer Science

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