An idea of a clustering algorithm using support vector machines based on binary decision tree

Abstract

Clustering is a technique which is commonly known in the domain of machine learning as an unsupervised method, it aims at constructing from a set of objects some different groups which are as homogeneous as possible. On the other hand support vector machines (SVM) and binary decision trees (BDT) were proposed and developed as supervised learning techniques where the output assembly is previously known. In this work we will try to build a clustering algorithm that uses the two supervised methods we cited above.