Density preservation and vector quantization in immune-inspired algorithms

Abstract

A clustering algorithm may be designed to generate prototypes capable of minimizing the cumulative distance between each sample in the dataset and its corresponding prototype, denoted as minimum quantization error clustering. On the other hand, some clustering applications may require density-preserving prototypes, more specifically prototypes that maximally obey the original density distribution of the dataset. This paper presents a conceptual framework to demonstrate that both criteria are attainable but are distinct and cannot be fulfilled simultaneously. Illustrative examples are used to validate the framework, further applied to produce an adaptive radius immune-inspired algorithm capable of transiting between both criteria in practical applications. © 2010 Springer-Verlag Berlin Heidelberg.