一种基于遗传算法的聚类集成方法

聚类集成算法通常对聚类成员差异性要求较高，导致算法在生成聚类成员阶段计算复杂度提高。针对该问题提出了一种基于遗传算法的聚类集成方法CEGA，不考虑聚类成员的差异性，而是利用目标函数将聚类问题转化为聚类成员的优化问题，充分利用遗传算法内在的并行性和全局寻优能力，对聚类成员进行优化组合，并以得到的最优染色体作为聚类集成最终结果。分析了CEGA的复杂度及适用范围，并利用UCI数据库中部分数据集进行实验，实验结果表明这种聚类集成方法的有效性。

New model for clustering ensemble based on genetic algorithms

Clustering ensemble algorithms require higher differences among clustering components, which induce higher complexity during the generating phase of clustering components. This paper proposes a new model for Clustering Ensemble based on Genetic Algorithm（CEGA）, which does not need to consider the differences between clustering components, but translates clustering into optimization of clustering components by calculating target function, and optimizes the grouping of clustering components by genetic algorithms. CEGA sets the final optimal chromosome to be the result of clustering and its complexity and application are also analyzed. Experimental results demonstrate the effectiveness of the proposed method on several UCI datasets.