一种SOFM网络的二阶段聚类算法

Two-phase Clustering Algorithm Based on Self-organizing Feature Maps

针对聚类数目未知情况下的聚类问题,提出了一种自组织特征映射网络(Self-organizing Feature Maps,SOFM)的二阶段聚类算法.首先通过SOFM网络的自组织学习过程对数据集进行粗聚类,将数据集划分为若干个簇,以获胜神经元代表每个簇内的所有样本;然后采用凝聚层次聚类的方法对获胜神经元进行再聚类,并以树状图的形式给出可视化聚类结果;最后综合两阶段聚类结果得到最终的聚类结果.实验结果表明,所提出的算法可以获得良好的聚类结果.

In order to achieve clustering when the number of clusters is unknown,a two-phase clustering algorithm based on Self-organizing Feature Maps（SOFM）is proposed.Firstly,the datasets are roughly clustered through the self-organizing learning process of SOFM.After that,the datasets are divided into several clusters.The winning neurons of SOFM stand for the samples in each cluster.Then,those winning neurons are re-clustered through the method of agglomerative hierarchical clustering,and the clustering results are shown in the form of dendrogram.Finally,based on these two clustering results,the final results are obtained.The experimental results show that the proposed algorithm has better clustering results.