Support vector machine based clustering: A review

Clustering is one of the most important data mining techniques, its objective is to regroup similar objects into groups, with the aim of maximizing the intra-cluster similarit and minimizing the inter-cluster similarity in unsupervised way. Recently, support-based clustering methods attracted a lot of attention, especially Support Vector Clustering (SVC) due to its capability to overcome the main hardships of classical clustering methods. SVC can easily handle complex shape clusters and identify their number without initialization. SVC undergoes on two main steps, training and labeling, the first one consist of solving a quadratic programming problem (QPP) to obtain a decision mathematics function, which is used in the next step to label all objects with their appropriate clusters. However, training an SVC model (solving a QPP) and labelling objects using huge data sets can lead to a high computation burden, in order to surmount this main issue and trying to improve the SVC performance, many methods and techniques was proposed in literature. In this paper, we aim to highlight and classify some of the most insightful works proposed by researchers according to their targeted SVC step.