Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data
technologies, has become one of the most pressing issues. In recent years, there has been …

Three classes of algorithms to learn the structure of Bayesian networks from data are
common in the literature: constraint-based algorithms, which use conditional independence …

Real world entities work in concert as a system and not in isolation. Understanding the
associations between these entities from their digital signatures can provide novel system …

Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-
Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and …

Data Mining Page 1 Data Mining Practical Machine Learning Tools and Techniques Third
Edition Ian H. Witten Eibe Frank Mark A. Hall ELSEVIER AMSTERDAM • BOSTON • …

In this paper, we provide new complexity results for algorithms that learn discrete-variable
Bayesian networks from data. Our results apply whenever the learning algorithm uses a …

Learning Bayesian networks is often cast as an optimization problem, where the
computational task is to find a structure that maximizes a statistically motivated score. By and …

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are
growing in popularity. Their versatility and modelling power is now employed across a …