Clustering ensemble is an algorithm to improve the accuracy, stability and robustness of clustering results. A better result can be produced by integrating multiple base clustering results.

The basic idea of this method is to cluster the original data set using multiple independent base clusterers, and then use some integration method to process it and obtain a final integration result.

In the first stage of this type of algorithm, multiple methods should be used as much as possible to obtain base clustering results; in the second stage, the most appropriate integrated solution should be selected to process these results.