A comparative study of clustering ensemble algorithms

作者：Wu, Xiuge; Ma, Tinghuai^*; Cao, Jie; Tian, Yuan; Alabdulkarim, Alia

来源：Computers & Electrical Engineering, 2018, 68: 603-615.

DOI：10.1016/j.compeleceng.2018.05.005

• Clustering ensemble
• Generative mechanism
• Consensus function
• Ensemble member
• Diversity
• Ensemble size

Since clustering ensemble was proposed, it has rapidly attracted much attention. This paper makes an overview of recent research on clustering ensemble about generative mechanism, selective clustering ensemble, consensus function and application. Twelve clustering ensemble algorithms are described and compared to choose a basic one. The experiment shows that using k-means with different initializations as generative mechanism and average-linkage agglomerative clustering as consensus function is the best one. As ensemble size increases, the performance of clustering ensemble improves. The basic clustering ensemble algorithm with suitable ensemble size is compared with clustering algorithms and the experiment shows that clustering ensemble is better than clustering. The influence of diversity on clustering ensemble is instructive to selecting members. The experiment shows that selecting members in high quality and big diversity for low-dimensional data sets, and selecting members in high quality and median diversity for high-dimensional data sets are better than traditional clustering ensemble.

• 出版日期2018-5
• 单位南京信息工程大学