상세정보

An expert guide for applying data mining with uncertain reasoning to a wide range of uses

This volume presents a holistic view of data mining by integrating this diverse and exciting field with uncertain reasoning. It treats a wide range of issues and examines the state of the art in both fields while summarizing vital concepts that can normally only be found in various separate resources.

The author concentrates on practical aspects of data mining-such as infrastructure and overall processes-but also discusses some selected algorithms and performance-related issues. Several important topics are addressed specifically, such as bridging the fields of machine learning and data mining and the discovery of influential association rules. In addition, the author discusses data warehousing as an enabling technique for data mining. Case studies are included throughout to illustrate important concepts.

Data Mining and Uncertain Reasoning is a practical reference for practitioners in various interrelated fields. Each subject is treated with both basic introductory and advanced technical descriptions, making the book suitable for students and practitioners at various levels of experience.

Dieses Buch behandelt die Theorie der unscharfen Folgerungen (uncertain reasoning) im Zusammenhang mit Data Mining, die anhand einer Reihe vollstandiger Fallstudien anschaulich erlautert wird. Eine große Auswahl von Zusatzliteratur rundet das Bild ab.

New feature

정보제공 :

What This Book Is About.
 
 Basics of Data Mining.
 
 Enabling Techniques and Advanced Features of Data Mining.
 
 Dealing with Uncertainty in Manipulation of Data.
 
 Data Mining Tasks for Knowledge Discovery.
 
 Bayesian Networks and Artificial Neural Networks.
 
 Uncertain Reasoning Techniques for Data Mining.
 
 Data Mining Lifecycle with Uncertainty Handling: Case Studies and Software Tools.
 
 Intelligent Conceptual Query Answering with Uncertainty: Basic Aspects and Case Studies.
 
 References.
 
 Index.

정보제공 :