Discovery And Fusion Of Uncertain Knowledge In Data

by Kun Yue (Author),‎ Weiyi Liu (Author),‎ Hao Wu (Author)

Data analysis is of upmost importance in the mining of big data, where knowledge discovery and inference are the basis for intelligent systems to support the real world applications. However, the process involves knowledge acquisition, representation, inference and data, Bayesian network (BN) is the key technology plays a key role in knowledge representation, in order to pave way to cope with incomplete, fuzzy data to solve the real-life problems.

This book presents Bayesian network as a technology to support data-intensive and incremental learning in knowledge discovery, inference and data fusion in uncertain environment.

Contents:
Introduction
Data-Intensive Learning of Uncertain Knowledge
Data-Intensive Inferences of Large-Scale Bayesian Networks
Uncertain Knowledge Representation and Inference for Lineage Processing over Uncertain Data
Uncertain Knowledge Representation and Inference for Tracing Errors in Uncertain Data
Fusing Uncertain Knowledge in Time-Series Data
Summary

Readership: Graduate students, researchers and professionals in the field of artificial intelligence/machine learning and information sciences, especially in databases.