Data Mining and Uncertain Reasoning: An Integrated Approach

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.

Spis treści:
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.

Nota biograficzna:
ZHENGXIN CHEN is Professor in the Department of Computer Science at the University of Nebraska.

Okładka tylna:
An expert guide for applying data mining with uncertai n 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.