Uncertainty Handling and Quality Assessment in Data Mining

Covers the key topics of assessing the quality of the results and handling uncertainty in data mining.

Focuses on the quality assessment of the results and the use of uncertainty in data mining rather than providing a general treatment of the subject of data mining

Texte du rabat

Uncertainty Handling and Quality Assessment in Data Mining provides an introduction to the application of these concepts in Knowledge Discovery and Data Mining. It reviews the state-of-the-art in uncertainty handling and discusses a framework for unveiling and handling uncertainty. Coverage of quality assessment begins with an introduction to cluster analysis and a comparison of the methods and approaches that may be used. The techniques and algorithms involved in other essential data mining tasks, such as classification and extraction of association rules, are also discussed together with a review of the quality criteria and techniques for evaluating the data mining results. This book presents a general framework for assessing quality and handling uncertainty which is based on tested concepts and theories. This framework forms the basis of an implementation tool, 'Uminer' which is introduced to the reader for the first time. This tool supports the key data mining tasks while enhancing the traditional processes for handling uncertainty and assessing quality. Aimed at IT professionals involved with data mining and knowledge discovery, the work is supported with case studies from epidemiology and telecommunications that illustrate how the tool works in 'real world' data mining projects. The book would also be of interest to final year undergraduates or post-graduate students looking at: databases, algorithms, artificial intelligence and information systems particularly with regard to uncertainty and quality assessment.

Contenu
Data Mining Process.- 2.1 Introduction to the Main Concepts of Data Mining.- 2.2 Knowledge and Data Mining.- 2.3 The Data Mining Process.- 2.4 Classification of Data Mining Methods.- 2.5 Overview of Data Mining Tasks.- 2.6 Summary.- References.- Quality Assessment in Data Mining.- 3.1 Introduction.- 3.2 Data Pre-processing and Quality Assessment.- 3.3 Evaluation of Classification Methods.- 3.4 Association Rules.- 3.5 Cluster Validity.- 3.6 Summary.- References.- Uncertainty Handling in Data Mining.- 4.1 Introduction.- 4.2 Basic Concepts on Fuzzy Logic.- 4.3 Basic Concepts on Probabilistic Theory.- 4.4 Probabilistic and Fuzzy Approaches.- 4.5 The EM Algorithm.- 4.6 Fuzzy Cluster Analysis.- 4.7 Fuzzy Classification Approaches.- 4.8 Managing Uncertainty and Quality in the Classification Process.- 4.9 Fuzzy Association Rules.- 4.10 Summary.- References.- UMiner: A Data Mining System Handling Uncertainty and Quality.- 5.1 Introduction.- 5.2 UMiner Development Approach.- 5.3 System Architecture.- 5.4 UMiner's Data Mining Tasks.- 5.5 Demonstration.- 5.6 Summary.- References.- Case Studies.- 6.1 Extracting Association Rules for Medical Data Analysis.- 6.2 The Mining Process.- 6.3 Cluster Analysis of Epidemiological Data.- References.