Uncertainty Handling and Quality Assessment in Data Mining

1. Front Matter

   Pages I-IX

   PDF

2. Introduction

   • Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos

   Pages 1-9

3. Data Mining Process

   • Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos

   Pages 11-71

4. Quality Assessment in Data Mining

   • Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos

   Pages 73-127

5. Uncertainty Handling in Data Mining

   • Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos

   Pages 129-181

6. UMiner: A Data Mining System Handling Uncertainty and Quality

   • Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos

   Pages 183-198

7. Case Studies

   • Michalis Vazirgiannis, Maria Halkidi, Dimitrios Gunopulos

   Pages 199-221

8. Back Matter

   Pages 223-226

   PDF

The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.

• Cluster Validity
• Data Mining
• Knowledge Discovery
• Quality Assessment
• Uncertainty Handling
• algorithms
• database
• data structures

• Department of Informatics, Athens University of Economics and Business, Greece

  Michalis Vazirgiannis, Maria Halkidi

• Department of Computer Science and Engineering, University of California, Riverside, USA

  Dimitrios Gunopulos

Policies and ethics