Open and closed systems in data analysis entropy

This dissertation presents an efficient and integrated approach to data analysis. Entropy data analysis is an evolution of reconstructability analysis, which investigates the relationships between parts and wholes. The central theme of the dissertation is the development of a system to condense the information in a data set into a small number of parameters. The new system is called a k-system. k-system analysis goes beyond traditional data analysis in that it has the potentiality for changes to be made to a system; and the impact of these changes can be evaluated. k-system can be useful in designing and evaluating open and closed systems. The behavior of these systems is measured by the k(.) function. The k-system is a closed system. The concept of open comes about when we try to reconstruct the k-system starting with an empty system which contains no information. We add information until we are satisfied that this constructed system adequately reproduces the k-system. The dissertation will identify and isolate the mathematics in the k-system algorithms that determine an open or closed system. It will change the algorithms to offer the option of an open or closed system, and then implement these changes in the k-system algorithms for option of an open or closed system.