Identification of Hidden Relationships among the Data Items by Using Data Mining Techniques

Abstract

We know that Data mining or Knowledge discovery from databases (KDD) is a collection of exploration techniques based on advanced analytical methods and tools for handling a large amount of information. Many data mining techniques are closely related to machine learning techniques and others are related to techniques that have been developed in statistics, sometimes called exploratory data analysis. In this article, we provide guidance for researchers on the use of structural equation modeling in practice for theory testing and development. It is used to measure the relationships between Independent variables (IV) and Dependent Variables (DV). Both IVs and DVs can be either measured variables (directly observed) or latent variables (unobserved, not directly observed). Structural equation modeling is also referred to as causal modeling, causal analysis, simultaneous equation modeling, analysis of covariance structures, path analysis, or confirmatory factor analysis. The latter two are actually special types of SEM. structural equation modeling (SEM) is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables / outline the basic elements of the SEM approach / provide researchers and students trained in basic inferential statistics a nontechnical introduction to SEM approach / refers to concepts from standard statistical approaches in the social and behavioral sciences such as correlation, multiple regression, and analysis of variance.

Key Words: KDD, Independent variable, Path analysis, SEM.