Using Simulated Annealing algorithm to improve ensemble clustering

Data clustering is one of the main tasks of data mining, which is responsible for exploring hidden patterns in unlabeled data. Due to the complexity of the problem and the weakness of the basic clustering methods, today most of the studies are directed towards clustering ensemble methods. Although for most datasets, there are individual clustering algorithms that provide acceptable results, but the ability of a single clustering algorithm is limited. In fact, the main purpose of clustering ensemble is to search for better and more stable results, using the combination of information and results obtained from several initial clustering. In this paper, a clustering ensemble-based method will be proposed, which, like most evidence accumulation methods, has two steps: 1- building a simultaneous participation matrix and 2- determining the final output from the proposed participation matrix. In the proposed method, some other information will be used in addition to the clustering of the samples to construct the simultaneous participation matrix. This information can be related to the degree of similarity of the samples, the size of the initial clusters, the degree of stability of the initial clusters, etc. In this paper, the clustering problem is defined as an explicit optimization problem by the mixed Gaussian model and is solved using the simulated annealing algorithm. Also, an evolutionary method based on simulated annealing will be presented to determine the final output from the proposed simultaneous participation matrix. The most important part of the evolutionary method is to determine the objective function that guarantees the final output will be of high quality. The experimental results show that the proposed method is better than other similar methods in terms of different clustering quality evaluation criteria.