Uncertainty Modeling for Data Mining A Label Semantics Approach /

• Main Authors: Qin, Zengchang (Author), Tang, Yongchuan (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014.
• Series: Advanced Topics in Science and Technology in China,
• Subjects: Computer science.; Computer science > Mathematics.; Computers.; Data mining.; Artificial intelligence.; Computer Science.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).; Information Systems and Communication Service.; Math Applications in Computer Science.

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 un...