Affiliations: [a] School of Mathematics and Physics, Shanghai University of Electric Power, Pudong New District, Shanghai, China | [b] School of Electronics and Information Engineering, Shanghai University of Electric Power, Pudong New District, Shanghai, China
Correspondence:
[*]
Corresponding author: Yuan Sun, School of Mathematics and Physics, Pudong New District, Shanghai 201306, China. E-mail: combmathe@shiep.edu.cn.
Abstract: As a research hotspot in ensemble learning, clustering ensemble obtains robust and highly accurate algorithms by integrating multiple basic clustering algorithms. Most of the existing clustering ensemble algorithms take the linear clustering algorithms as the base clusterings. As a typical unsupervised learning technique, clustering algorithms have difficulties properly defining the accuracy of the findings, making it difficult to significantly enhance the performance of the final algorithm. AGglomerative NESting method is used to build base clusters in this article, and an integration strategy for integrating multiple AGglomerative NESting clusterings is proposed. The algorithm has three main steps: evaluating the credibility of labels, producing multiple base clusters, and constructing the relation among clusters. The proposed algorithm builds on the original advantages of AGglomerative NESting and further compensates for the inability to identify arbitrarily shaped clusters. It can establish the proposed algorithm’s superiority in terms of clustering performance by comparing the proposed algorithm’s clustering performance to that of existing clustering algorithms on different datasets.
Keywords: Clustering ensemble, non-convex clusters, AGglomerative NESting, local hypothesis
