Proceedings of the International Conference on Cognition and Recognition Detection of Monotonic Chain Structures in Mixed Feature Type Multidimensional Data

Abstract

Symbolic data analysis aims at generalizing some standard statistical methods. Generalization of principal component analysis (PCA) is an interesting and important research problem in symbolic data analysis. A main purpose of PCA is to find a linear structure in multidimensional data. However, a direct extension of PCA is difficult, when each object is described by not only usual quantitative features but also interval valued features and qualitative features. This paper describes a method to detect “monotonic chain structures ” including “linear structure ” based on the Cartesian system model (CSM) which is a mathematical model to manipulate objects described by mixed type feature values. Simple examples are presented to illustrate our approach. 1