Uncertainty Handling and Quality Assessment in Data Mining

Softcover Nachdruck of the original 1. Auflage 2003
ISBN: 978-1-4471-1119-1
Verlag: Springer

Buch, Englisch, 226 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 371 g

Reihe: Advanced Information and Knowledge Processing

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.

Zielgruppe

Research

Autoren/Hrsg.

Vazirgiannis, Michalis

Gunopulos, Dimitrious

Halkidi, Maria

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie

Inhaltsverzeichnis

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.