IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Visualization Techniques for Data Mining

Visualization Techniques for Data Mining
View Sample PDF
Author(s): Herna L. Viktor (University of Ottawa, Canada)and Eric Paquet (National Research Council of Canada, Canada)
Copyright: 2008
Pages: 8
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch093

Purchase

View Visualization Techniques for Data Mining on the publisher's website for pricing and purchasing information.

Abstract

The current explosion of data and information, mainly caused by data warehousing technologies as well as the extensive use of the Internet and its related technologies, has increased the urgent need for the development of techniques for intelligent data analysis. Data mining, which concerns the discovery and extraction of knowledge chunks from large data repositories, is aimed at addressing this need. Data mining automates the discovery of hidden patterns and relationships that may not always be obvious. Data mining tools include classification techniques (such as decision trees, rule induction programs and neural networks) (Han & Kamber, 2001), clustering algorithms and association rule approaches, amongst others.

Related Content

Nuno Silva, Pedro Sousa, Miguel Mira da Silva. © 2019. 19 pages.
Ioannis Routis, Mara Nikolaidou, Nancy Alexopoulou. © 2019. 21 pages.
Jeffrey S. Zanzig, Guillermo A. Francia III, Xavier P. Francia. © 2019. 26 pages.
S. B. Goyal. © 2019. 30 pages.
Maria João Ferreira, Fernando Moreira, Isabel Seruca. © 2019. 24 pages.
Agostino Poggi, Paolo Fornacciari, Gianfranco Lombardo, Monica Mordonini, Michele Tomaiuolo. © 2019. 21 pages.
Rüdiger Pryss, Manfred Reichert. © 2019. 26 pages.
Body Bottom