基于正序迭代选择策略的聚类中心自动选择方法

针对密度峰值聚类算法的决策函数不能自动有效地确定聚类中心的问题,提出自动确定聚类中心的密度峰值聚类算法.首先,通过归一化处理,使决策函数中的两个变量分布均匀.然后,在确定聚类中心时,提出正序迭代选择策略,即根据聚类核心点数目的变化趋势搜索拐点,并以拐点之前的点作为聚类中心,完成聚类.最后,在UCI数据集上验证文中算法的性能,算法在未提高时间复杂度的情况下,可以对任意分布形状的数据集进行聚类,具有较好的适应性和聚类效果.

Automatic Selection Method of Cluster Center Based on Positive Sequence Iterative Selection Strategy

The decision function of density peak clustering algorithm cannot determine the clustering center automatically and effectively. Therefore, a density peak clustering algorithm, automatically clustering by fast search and find of density peaks(AUTO-CFSFDP), is proposed. Firstly, the normalization process is carried out to make the uneven distribution of variables in the decision function become uniform. Secondly, the selection strategy based on positive-sequence iteration is presented to search elbow point according to the variation trend of the number of cluster core points in the process of determining the cluster center. A set of points before the elbow point is used as the cluster centers to complete clustering. Finally, the performance of AUTO-CFSFDP is evaluated on UCI datasets. AUTO-CFSFDP can cluster the datasets of arbitrary distributions without extra time consumption. The adaptability and clustering results are improved effectively.