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Proceedings Paper

Selection of data analysis techniques for data mining applications
Author(s): Rashpal S. Ahluwalia; Sundar Chidambaram
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Paper Abstract

Multivariate statistical techniques are used to analyze complex data sets with many independent and dependent variables. The dataset may be analyzed for relationships among variables based on correlation, significance of group differences based on variance and covariance, prediction of group membership, and prediction of empirical or theoretical structure of the data. The choice among the available multivariate analysis techniques for each of these research questions is based on the nature of the variables, the number of independent and dependent variables and if the independent variables can be considered as covariates. This paper describes a software tool that can assist researchers in selecting the appropriate data analysis technique based on the research needs of the data. The data analyses techniques discussed in this paper are discriminant function analysis, multi-way frequency analysis and logistic regression. The structure underlying a dataset is based on multivariate approaches such as principal components analysis, factor analysis and structural equation modeling. The paper illustrates the software tool on the Fisher's Iris data set.

Paper Details

Date Published: 11 November 2004
PDF: 12 pages
Proc. SPIE 5605, Intelligent Systems in Design and Manufacturing V, (11 November 2004); doi: 10.1117/12.572989
Show Author Affiliations
Rashpal S. Ahluwalia, West Virginia Univ. (United States)
Sundar Chidambaram, West Virginia Univ. (United States)


Published in SPIE Proceedings Vol. 5605:
Intelligent Systems in Design and Manufacturing V
Bhaskaran Gopalakrishnan, Editor(s)

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