Knowledge Structure and Data Mining Techniques

View Sample PDF

Author(s): Rick L. Wilson (Oklahoma State University, USA), Peter A. Rosen (University of Evansville, USA)and Mohammad Saad Al-Ahmadi (Oklahoma State University, USA)
Copyright: 2008
Pages: 9
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.ch002

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

Purchase

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

Abstract

Considerable research has been done in the recent past that compares the performance of different data mining techniques on various data sets (e.g., Lim, Low, & Shih, 2000). The goal of these studies is to try to determine which data mining technique performs best under what circumstances. Results are often conflicting—for instance, some articles find that neural networks (NN) outperform both traditional statistical techniques and inductive learning techniques, but then the opposite is found with other datasets (Sen & Gibbs, 1994; Sung, Chang, & Lee, 1999: Spangler, May, & Vargas, 1999). Most of these studies use publicly available datasets in their analysis, and because they are not artificially created, it is difficult to control for possible data characteristics in the analysis. Another drawback of these datasets is that they are usually very small.

The IRMA Community

Research IRM

Knowledge Structure and Data Mining Techniques

Purchase

Abstract

Related Content

IRMA Sponsors