Uncertainty Handling and Quality Assessment in Data Mining
Published on 4. October 2012
Book
Paperback/Softback
IX, 226 pages
978-1-4471-1119-1 (ISBN)
Description
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.
More details
Series
Edition
Softcover reprint of the original 1st ed. 2003
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Research
Illustrations
IX, 226 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 14 mm
Weight
371 gr
ISBN-13
978-1-4471-1119-1 (9781447111191)
DOI
10.1007/978-1-4471-0031-7
Schweitzer Classification
Other editions
Additional editions
Michalis Vazirgiannis | Maria Halkidi | Dimitrious Gunopulos
Uncertainty Handling and Quality Assessment in Data Mining
Book
07/2003
Springer
€53.49
Shipment within 15-20 days
Content
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.