An automatic clustering method using multi-objective genetic algorithm with gene rearrangement and cluster merging

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As an unsupervised approach of machine learning, clustering is an important method to understand and learn structural information from data. However, current adaptive clustering approach based on multi-objective genetic algorithm have two apparent limitations. The first is that prior knowledge, i.e., sample information is needed to get the correct cluster number. The second is that no effective method can be found to select the best clustering solution from the Pareto Optimal Front (POF) generated by a multi-objective optimization. These problems become severer in applications applied on non-category datasets. Therefore, the primary goal of this research is to establish a genetic optimization based multi-objective clustering framework, in which multiple clustering validity indexes (CVIs) can be tested simultaneously to automatically obtain the optimal cluster number without knowing any sample label information in advance. In this effort, we will not only be able to consider clustering measurements such as cluster cohesion and separation, but also take other aspects, such as compactness, connectivity, variation among data elements, into consideration as well. Then, we aim to design a procedure to recommend three best solutions from the POF by using appropriate combination of CVIs without increasing computational cost. This procedure is expected to control the cluster number in a reasonable range and consequently decrease the difficulty in best solution recommendation. Finally, since we have the knowledge that using gene rearrangement in the genetic optimization does not affect partition, we take this advantage to merge clusters effectively and significantly speed the convergence of the algorithm. Our approach can outperform the state-of-the-art counterparts across diverse benchmark datasets in terms of partitioning accuracy and performance, as demonstrated in three experiments conducted on both artificial and typical real-world datasets.