Rule Learning

Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.

A Quick Way to Learn & Implement Association Rule Mining Learning Algorithms for Recommendation Engine Systems in Python

by Han Liu (Author), Alexander Gegov (Author), Mihaela Cocea (Author)
Presents a novel theory of rule based systems in machine learning context
Introduces ways of big data processing by rule learning algorithms for knowledge discovery and predictive modelling in classification tasks
Focuses on introducing effective ways to address the issues relating to predictive accuracy, computational complexity and interpretability of rule based systems for classification
Some popular methods and techniques, which can be used as components of the framework, are described and justified in detail
Explores explicitly the connections between rule based systems and machine learning in a conceptual context

Unlock the potential of AI with our Machine Learning course – master essential techniques and algorithms today!

Learn 16 Machine Learning Algorithms in a Fun and Easy along with Practical Python Labs using Keras

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

Building Intelligent Systems from the Ground Up

Deep dive into Machine Learning with Python Programming. Implement practical scenarios & a project on Recommender System.

Learn about Data Mining Standard Processes, Survival Analysis, Clustering Analysis, Various algorithms and much more.