Multi-View Ensemble Clustering Algorithm Weighted by Kernel Density Estimation

Most of the existing ensemble clustering algorithms improve the performance by weighting the basic clusters to reduce the influence of low-quality basic clusters on the final clustering results. Low-quality base clustering can be understood as misclassifying sample points, which are presented as discrete points in the co-association matrix. So, we proposed a multi-view ensemble clustering algorithm based on the weighting of kernel density estimation, starting from the density distribution of discrete points in the co-association matrix. Firstly, the different sets of basic clusters are made into a co-association matrix using the evidence accumulation model. Secondly, we converted the co-association matrix into a sparse matrix, and then we calculated the density distribution weights of the nonzero elements in the matrix using kernel density estimation. Next we let these weights multiply with the sparse matrix. Thirdly, we reduce the weighted sparse matrix to the shape of the initial co-association matrix to obtain the density-weighted co-association matrix. Next, we use the K-means clustering algorithm on the weighted co-association matrix to obtain the final clustering results. Finally, the algorithm is subjected to comparison experiments and ablation experiments on five commonly used datasets. The experimental results show that the proposed ensemble clustering algorithm with kernel density estimation weighting performs better than other comparative algorithms.