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

Effective Intelligent Data Mining Using Dempster-Shafer Theory

Effective Intelligent Data Mining Using Dempster-Shafer Theory
View Sample PDF
Author(s): Malcolm J. Beynon (Cardiff University, UK)
Copyright: 2008
Pages: 21
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.ch188

Purchase

View Effective Intelligent Data Mining Using Dempster-Shafer Theory on the publisher's website for pricing and purchasing information.

Abstract

The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is the quality of the data analysed, including whether the data is imprecise or in the worst case incomplete. Through the description of Dempster-Shafer theory (DST), a general methodology based on uncertain reasoning, it argues that traditional data mining techniques are not structured to handle such imperfect data, instead requiring the external management of missing values, and so forth. One DST based technique is classification and ranking belief simplex (CaRBS), which allows intelligent data mining through the acceptance of missing values in the data analysed, considering them a factor of ignorance, and not requiring their external management. Results presented here, using CaRBS and a number of simplex plots, show the effect of managing and not managing of imperfect data.

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