基于混合度量与类簇自适应调整的粗糙模糊K-means聚类算法

针对粗糙K-means聚类及其相关衍生算法需要提前人为给定聚类数目、随机选取初始类簇中心导致类簇交叉区域的数据划分准确率偏低等问题,文中提出基于混合度量与类簇自适应调整的粗糙模糊K-means聚类算法.在计算边界区域的数据对象归属于不同类簇的隶属程度时,综合考虑局部密度和距离的混合度量,并采用自适应调整类簇数目的策略,获得最佳聚类数目.选取数据对象稠密区域中距离最小的两个样本的中点作为初始类簇中心,将附近局部密度高于平均密度的对象划分至该簇后再选取剩余的初始类簇中心,使初始类簇中心的选取更合理.在人工数据集和UCI标准数据集上的实验表明,文中算法在处理类簇交叠严重的球簇状数据集时,具有自适应性,聚类精度较优.

Rough Fuzzy K-means Clustering Algorithm Based on Mixed Metrics and Cluster Adaptive Adjustment

Rough K-means clustering and its related derivative algorithms need the number of clusters in advance, and random selection of the initial cluster center results in low accuracy of data partition in the cross-region of clusters. To solve these problems, a rough fuzzy K-means clustering algorithm with adaptive adjustment of clusters is proposed. When the membership degree of the data objects belonging to different clusters in the intersection area of the cluster boundary is calculated, the mixed metrics of local density and distance are taken into account in the proposed algorithm. The optimal number of clusters is gained by adjusting the number of clusters adaptively. The midpoint of two samples with the smallest distance in the dense area of data objects is selected as the initial cluster center. The object with the local density higher than the average density is divided into the cluster, and then the re-maining initial cluster center can be selected. Thus, the selection of the initial cluster centers is more reasonable. The experiments on synthetic datasets and UCI datasets demonstrate the advantages of the proposed algorithm in adaptability and clustering accuracy for dealing with spherical clusters with blurred boundaries.