Ensemble Clustering Algorithm Based on Weighted Super Cluster

Abstract

Most ensemble clustering algorithms use K-means to generate base clustering, but the result of base clustering is not good. And most ensemble clustering algorithms ignore the diversity of base clustering, treat base clustering equally, and generate the co-association matrix on the samples. When the number of samples or integration scale is large, the computational burden increases significantly. To solve the above problems, an ensemble clustering algorithm based on weighted super cluster (ECWSC) is proposed. This algorithm combines random selection with K-means selection to obtain landmarks sampling, and uses spectral clustering algorithm for landmarks to get the clustering result. Then, the samples are mapped to the nearest landmark points to get the base clustering. On this basis, the uncertainty of the base clustering is calculated, and the corresponding weight is given. Then the co-association matrix based on weighted super cluster is obtained by weighted method, and the integration result is obtained by using hierarchical clustering algorithm. 7 real datasets and 4 artificial datasets are selected as experimental datasets to verify the accuracy, robustness and time complexity of the methods. Experimental results show that this algorithm can effectively improve the ensemble clustering effect.

Keywords

• landmark sampling;
• spectral clustering;
• ensemble clustering;
• co-association matrix;
• weighted strategy