Research Progress on Semi-Supervised Clustering

Abstract

Semi-supervised clustering is a new learning method which combines semi-supervised learning (SSL) and cluster analysis. It is widely valued and applied to machine learning. Traditional unsupervised clustering algorithm based on data partition does not need any property; however, there are a small amount of independent class labels or pair constraint information data samples in practice; in order to obtain better clustering results, scholars have proposed a semi-supervised clustering. Compared with traditional clustering methods, it can effectively improve clustering performance through a small number of supervised information, and it has been used widely in machine learning. Firstly, this paper introduces the research status and classification of semi-supervised learning and compares the four classification methods as follows: decentralized model, support vector machine, graph, and collaborative training. Secondly, the semi-supervised clustering is described in detail, the current status of semi-supervised clustering is analyzed, and the Cop-kmeans algorithm, Lcop-kmeans algorithm, Seeded-kmeans algorithm, SC-kmeans algorithm, and other algorithms are introduced. The introduction of several semi-supervised clustering methods in this paper can show the advantages of semi-supervised clustering over traditional clustering, and the related literature in recent years is summarized. This paper summarized the latest development of semi-supervised learning and semi-supervised clustering and discussed the application of semi-supervised clustering and the future research direction.