Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Combining multiple clusterings using similarity graph
Pattern Recognition
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
CLICOM: Cliques for combining multiple clusterings
Expert Systems with Applications: An International Journal
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In this paper we propose a clustering ensemble algorithm based on genetic algorithm. The most important feature of our method is ability to extract the number of clusters. Genetic algorithms have been known as methods with high ability to find the solution of optimization problems. One of these problems is clustering, a process that receives a dataset as input and divides its members into several subsets called cluster (partition or group). The members of each cluster would be alike while members of two different clusters would be as different as possible. One of the common ways to do this is combinational clustering. Combinational clustering will combine the results of different clustering methods or some executions of a clustering method to calculate final clusters. In this paper, an evolutionary combinational clustering method is proposed to find the number of clusters. The evaluation of this method on several common datasets shows the proper performance of proposed method to find final clusters as well as the exact number of clusters