两阶段模糊c-均值聚类算法及其应用

针对模糊c-均值算法对初始值敏感、收敛结果易陷入局部极小值的缺点,提出了两阶段模糊c-均值聚类算法.首先通过恰当的贴近度(满足相似相近性)估计分类数,选取初始聚类中心;然后通过模糊c-均值算法进行聚类,最后对所得的聚类中心采用逻辑斯谛型的灰色模型进行预测.由于聚类中心具有统计特征,因此较好地克服了样本间的随机误差,灰色逻辑斯谛模型较好地克服了每个样本内误差.采用上述方法对全国30个省市农村居民年收入进行了分析和比较,得出了具有参考价值的结果.

Two-stage fuzzy c-mean cluster and its applications

Fuzzy c-mean algorithm is sensitive to the starting value and its restraining result is easy to fall into the partial minimum.Thus,two-stage fuzzy c-mean cluster algorithm is proposed.First through the appropriate similar measure(satisfies similarity and nearness) to estimate the classified number,the selection initial cluster center,carries on the cluster again through the fuzzy c-mean algorithm,finally uses the gray Logistic model to the obtained cluster center to carry on the forecast.Because the cluster center has the statistical characteristic,preferably overcome random error between the samples,in gray Logistic model preferably overcome each sample the error,the use above model has carried on the forecast to the cluster center to be possible to achieve the proper attention to both function.Used the above method to carry on the analysis and the comparison to the national 30 provinces and cities countryside inhabitant yearly income.