Exemplars can Reciprocate Principal Components

This paper presents a clustering algorithm that is an extension of the Category Trees algorithm. method creates tree structures branch on category type and not feature. The development in this to consider secondary order which data row belongs, but tree, representing single classifier, it eventually clustered with. Each branches store subsets other categories, rows those may also be related. therefore concerned with looking at second level between subsets, try determine if there any consistency over it. It argued Principal Components related reciprocal structure, even bigger question about relation exemplars principal components, general. theory demonstrated using Portugal Forest Fires dataset as case study. are then combined Self-Organising algorithms from author suggested they all belong same family type, Entropy-style classifier.