A Generative Clustering Ensemble Model and Its Application in IoT Data Analysis

• Published in: Wireless communications and mobile computing 2022-06, Vol.2022, p.1-14
• Main Authors: Du, Hangyuan, Wang, Wenjian, Bai, Liang, Feng, Jinsong
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Data analysis; Graph representations; Graphical representations; Internet of Things; Learning; Methods; Optimization; Optimization models; Probabilistic models
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/8081177

Data analysis is the foundation of Internet of Things (IoT) based applications, and clustering is an effective technology of data analysis. Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the clustering performance in stability and robustness. However, it is difficult for existing clustering ensemble algorithms to achieve a satisfying ensemble result, when the base clustering results are unreliable. Concerning this problem, we develop a new clustering ensemble model in this paper, which has several advantages compared with traditional algorithms: (i) structure information about the data is effectively extracted from the base clusterings; (ii) data characteristics and structure information are integrated in an elegant fashion, in the production of the consensus clustering result; and (iii) our model has the generative ability that makes the model achieve outstanding performance when training samples are insufficient. In our model, the structural information is extracted by explicating the coupling relationships between base clusterings and between samples in clustering members. Then, data characteristics and structure information are combined in a generative graph representation learning framework. And the objectives of representation learning and consensus clustering are integrated into a unified optimization model, in which the prior distribution of the data is approximated by a Gaussian mixture model (GMM). Extensive experiments are conducted on multiple IoT datasets; the results prove that our model not only performs better than the conventional clustering ensemble algorithms but also outperforms the state-of-the-art deep clustering methods.