基于局部密度自适应度量的粗糙K-means聚类算法

通过引入上、下近似的思想,粗糙K-means已成为一种处理聚类边界模糊问题的有效算法,粗糙模糊K-means、模糊粗糙K-means等作为粗糙K-means的衍生算法,进一步对聚类边界对象的不确定性进行了细化描述,改善了聚类的效果。然而,这些算法在中心均值迭代计算时没有充分考虑各簇的数据对象与均值中心的距离、邻近范围的数据分布疏密程度等因素对聚类精度的影响。针对这一问题提出了一种局部密度自适应度量的方法来描述簇内数据对象的空间特征,给出了一种基于局部密度自适应度量的粗糙K-means聚类算法,并通过实例计算分析验证了算法的有效性。

Rough K-means clustering based on local density adaptive measure

By introducing the idea of lower and upper approximations, rough K-means has become a powerful algorithm for clustering analysis with overlapping clusters. Its derivative algorithms such as rough fuzzy K-means and fuzzy rough K-means describe the uncertain objects located in the boundaries in detail, thus improving the clustering effect. However, these algorithms do not fully consider the influence of the factors, such as the distance between the data centers of the clusters and the average center and the density of the data distributed in the neighborhood, on the clustering accuracy. Aiming at this problem, a local density adaptive measure method is proposed to describe the spatial characteristics of data objects in a cluster. A rough K-means clustering algorithm based on local density adaptive measure is given. Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.