An Ensemble of Locally Reliable Cluster Solutions

Applied Sciences

Hamid Parvin¹  Amin Beheshti²  Nasim Khozouie³  MohammadReza Mahmoudi⁴  Huan Niu⁵  Hamid Alinejad-Rokny⁶ 

[1] Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani 7351, Iran;Department of Computing, Macquarie University, Sydney 2109, Australia;Department of computer Engineering, Faculty of Engineering, Yasouj University, Yasouj 759, Iran;Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;School of Information and Communication Engineering, Communication University of China, Beijing 100024, China;Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney 2052, Australia;

关键词: ensemble learning;    ensemble clustering;    kmedoids clustering;    local hypothesis;   

DOI  :  10.3390/app10051891

来源: DOAJ

【 摘 要 】

Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.

【 授权许可】

Unknown