Kernel-Induced Bubble Agglomeration Algorithm for unsupervised classification: An improved clustering methodology without prior information

Abstract

This paper introduces an improved unsupervised clustering algorithm, named Kernel-Induced Bubble Agglomeration. In this paper, the conventional Bubble Agglomeration algorithm is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance instead of the conventional sum-of-squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high dimensional feature space. Application of the conventional Bubble Agglomeration algorithm and the Kernel-induced Bubble Agglomeration algorithm to well-known data sets showed the superiority of the proposed approach.