ENGLISH

Uncertainty Modeling for Data Mining: A Label Semantics Approach

By Yongchuan Tang , Zengchang Qin

Book information

Publisher
Springer
Year
2015
ISBN
3642412505, 9783642412509
Language
english
Format
PDF
Filesize
7 MB (6946283 bytes)
Series
Advanced Topics in Science and Technology in China
Edition
2014
Pages
420\303
Scanned
yes
Time added
2014-11-05 22:48:42
Информатика и вычислительная техника;Искусственный

Description

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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