By Prof. Zengchang Qin, Prof. Yongchuan Tang (auth.)
Machine studying and information mining are inseparably hooked up with uncertainty. The observable facts for studying is generally obscure, incomplete or noisy. Uncertainty Modeling for facts Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based concept for modeling uncertainty. numerous new info mining algorithms in keeping with label semantics are proposed and demonstrated on real-world datasets. A prototype interpretation of label semantics and new prototype-based facts mining algorithms also are mentioned. This publication deals a helpful source for postgraduates, researchers and different pros within the fields of information mining, fuzzy computing and uncertainty reasoning.
Zengchang Qin is an affiliate professor on the university of Automation technological know-how and electric Engineering, Beihang college, China; Yongchuan Tang is an affiliate professor on the collage of computing device technological know-how, Zhejiang collage, China.