An Ensemble Clustering Method Based on Several Different Clustering Methods

Abstract As an unsupervised learning method, clustering is done to find natural groupings of patterns, points, or objects. In clustering algorithms, an important problem is the lack of a definitive approach based on which users can decide which clustering method is more compatible with the input data set. This problem is due to the use of special criteria for optimization. Cluster consensus, as the reuse of knowledge, provides a solution to solve the inherent challenges of clustering. Ensemble clustering methods have come to the fore with the slogan that combining several weak models is better than a strong model. This paper proposed the optimal K-Means Clustering Algorithm (KMCE) method as an ensemble clustering method. This paper has used the K-Means weak base clustering method as base clustering. Also, by adopting some measures, the diversity of the consensus has increased. The proposed ensemble clustering method has the advantage of K-Means, which is its speed. Also, it does not have its major weakness, which is the inability to detect non-spherical and non-uniform clusters. In the experimental results, we meticulously evaluated and compared the proposed hybrid clustering algorithm with other up-to-date and powerful clustering algorithms on different data sets, ensuring the robustness and reliability of our findings. The experimental results indicate the superiority of the proposed hybrid clustering method over other clustering algorithms in terms of F1-score, Adjusted rand index, and Normal mutual information.