基于自适应权重的粗糙K均值聚类算法

原有Rough K-means算法中类的上、下近似采用固定经验权重，其科学性值得商榷，针对这一问题，设计了一种基于自适应权重的粗糙K均值聚类算法。基于自适应权重的粗糙聚类算法在每一次迭代过程中，根据当前的数据划分状态，动态计算每个样本对于类的权重，降低了原有算法对初始权重的依赖。此外，该算法采用近似集合中的高斯距离比例来表现样本权重，从而可以在多种数据分布上得到更精确的聚类结果。实验结果表明，基于自适应权重的粗糙K均值算法是一种较优的聚类算法。

Rough K-means Clustering Based on Self-adaptive Weights

The fixed weights are adopted in the traditional rough K-means algorithm to represent the different approximations of the clusters, but it is always difficult to predefine the optimal weights with little priori knowledge before clustering. Therefore,an improved rough K-means algorithm based on self-adaptive weights was proposed in this paper.The new method computes the weights for every data according to the current clustering state and no more does rely on the initial weights. Furthermore, the self-adaptive weights arc obtained from the Gaussian distance ration in cluster approximation, which can lead to the more accurate clustering results. The experiments indicate that the rough K-means based on self-adaptive weights is an effective rough clustering algorithm.