An Optimization-based Clustering Algorithm for Nominal Scale Variants
Bai Shuo · Microelectronics & Computer · 2003
Traditional clustering techniques such as systemic clustering and K-means clustering adapt to interval scale variants, but not suit nominal scale variants. In this article, we study the clustering problem of nominal scale variants and define a kind of reversal distance to measure nominal scale basing on cognition psychology and optimization learning. By reversal distance measure, we propose a new clustering algorithm for nominal scale variants aiming at optimization of a target function, and then we demonstrate the clustering process of our algorithm. Experiments show that the result of our algorithm can be interpreted reasonably.