基于多目标进化算法的多距离聚类研究

传统的聚类算法通常基于单一的距离度量而设计，如何将多种距离度量有机融合在一起是当前面临的一个挑战。提出了一种基于多目标进化算法的多距离度量聚类框架(multiobjective evolutionary multiple distance measure clustering，MOMDC)，并使用欧氏距离和Path距离来设计实际框架。该框架首先将数据集分别用两种距离测度预聚类，而后将预聚类结果做合并，以降低问题的规模；其次分别计算子类间的两种距离关系；最后使用多目标进化算法在两种距离空间中并行聚类。在多目标进化算法设计中，使用实数-标签的编码方式来设计染色体，并且设计了基于两种距离测度的两个适应度函数对染色体进行评估。最终将MOMDC与其他几种经典算法在大量的数据集上进行实验对比。实验表明，该框架对不同分布的数据集均能取得良好的结果。

Research on multiple distance clustering based on multi-objective evolutionary algorithm

Traditional clustering algorithms often based on a single distance metric, and how to integrate multivariate metrics is a key challenge in clustering algorithms. This paper proposes a multiobjective Evolutionary Multiple Distance Measure Clustering (MOMDC) based on multi-objective evolutionary algorithm. In this paper, using the Euclidean distance and Path distance to design the actual framework. Firstly, The framework uses the two distance measures to preprocess the classes, and then combining the prepolymerization results to reduce the size of the problem. Secondly, using the multi-objective evolutionary algorithm to cluster in two distance spaces in parallel. In the design of multi - objective evolutionary algorithm, chromosomes using real - tag coding, and two fitness functions based on two distance measures are designed to evaluate the chromosomes. Finally, MOMDC will compare to several other classic algorithms in the data set . Experiments show that the framework can achieve good results for different distributed data sets.