Matthias Steinbrecher Libros

This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Features: provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools; contains numerous examples and definitions throughout the text; presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks; covers the latest approaches, including ant colony optimization and probabilistic graphical models; written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry.

Data storage space comes almost at no costs today. Accumulating data is therefore an ubiquitous task in basically every business organization. However, this collection process needs to be complemented with sophisticated data analysis techniques in order to detect patterns inside these data. Such patterns may indicate problems or opportunities. In both cases it is of paramount importance to detect the formation and development of such patterns early enough in order to take timely countermeasures. To reach a large range of users, such analysis methods have to be intuitively controllable, must provide instant feedback and offer suitable visualizations. In this thesis, I propose a framework to visualize and filter the temporal evolution of sets of association rules. I will show how linguistic terms (represented by fuzzy sets) can be used to quantify a rule's history (w. r. t. certain quantitative measures) and subsequently rank them to present only the most relevant ones to the user for further assessment. I will transfer the suggested filtering method to other model types, present the software platform on which the methods are implemented and provide empirical evaluations on real-world business data.