Uncertainty Handling and Quality Assessment in Data Mining
Authors: Vazirgiannis, Michalis, Halkidi, Maria, Gunopulos, Dimitrious
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- About this Textbook
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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.
- Table of contents (6 chapters)
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Introduction
Pages 1-9
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Data Mining Process
Pages 11-71
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Quality Assessment in Data Mining
Pages 73-127
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Uncertainty Handling in Data Mining
Pages 129-181
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UMiner: A Data Mining System Handling Uncertainty and Quality
Pages 183-198
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Table of contents (6 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Uncertainty Handling and Quality Assessment in Data Mining
- Authors
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- Michalis Vazirgiannis
- Maria Halkidi
- Dimitrious Gunopulos
- Series Title
- Advanced Information and Knowledge Processing
- Copyright
- 2003
- Publisher
- Springer-Verlag London
- Copyright Holder
- Springer-Verlag London
- eBook ISBN
- 978-1-4471-0031-7
- DOI
- 10.1007/978-1-4471-0031-7
- Hardcover ISBN
- 978-1-85233-655-4
- Softcover ISBN
- 978-1-4471-1119-1
- Series ISSN
- 1610-3947
- Edition Number
- 1
- Number of Pages
- IX, 226
- Topics
