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Uncertainty Handling and Quality Assessment in Data Mining

  • Textbook
  • © 2003

Overview

Authors:
  1. Michalis Vazirgiannis

    1. Department of Informatics, Athens University of Economics and Business, Greece

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  2. Maria Halkidi

    1. Department of Informatics, Athens University of Economics and Business, Greece

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    You can also search for this author in PubMed   Google Scholar

  3. Dimitrios Gunopulos

    1. Department of Computer Science and Engineering, University of California, Riverside, USA

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  • 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

Part of the book series: Advanced Information and Knowledge Processing (AI&KP)

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Table of contents (6 chapters)

  1. Front Matter

    Pages I-IX
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  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
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Keywords

About this book

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.

Authors and Affiliations

  • 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

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