A review on semi-supervised clustering

Semi-supervised clustering (SSC), a technique integrating semi-supervised learning and clustering analysis, incorporates the given prior information (e.g., class labels and pairwise constraints) into clustering to guide the clustering process and improve the performance. In recent years, a large number of valuable works have emerged, focusing on theoretical research and application in different fields. In this paper, a detailed review of SSC is provided from a new perspective. Firstly, all SSC studies are organized as partition-based SSC, hierarchical-based SSC, density-based SSC, graph-based SSC, neural network-based SSC, Nonnegative Matrix Factorization-based SSC and random subspace technique-based SSC. Thus, the semi-supervised researches can be in-depth discussed in each clustering idea. Secondly, the general overviews are detailed in each category respectively, including the performance, the suitable scenarios and the way to add supervising information. Thirdly, the recent successful applications of SSC are summarized according to different backgrounds such as medical, biological, business, journalism, financial and so on. Based on this, some application caveats and development trends of SSC are particularly given in the end. This comprehensive review and analysis of SSC can provide an overall outline, the scope of research topics, and a relative complete analysis of existing SSC methods for researchers.

Automated summary

Semi-supervised clustering (SSC) is a technique that integrates semi-supervised learning and clustering analysis to improve clustering performance by incorporating prior information. This paper provides a comprehensive review of SSC, organized into different categories and discusses their performance, suitable scenarios, and ways to add supervising information. It also summarizes successful applications of SSC in various fields and provides application caveats and development trends. This review and analysis of SSC can benefit researchers in providing an overall understanding, research topics, and analysis of existing methods.