基于互信息和散度改进K-Means在交通数据聚类中的应用

K-means算法是一种常用的聚类算法,已应用于交通热点提取中.但是,由于聚类数目和初始聚类中心的主观设置,已有的聚类方法提取的交通热点往往难以满足要求.利用互信息和相对熵,提出SK-means算法,并应用于交通热点提取中.在所提方法中,基于不同点之间的互信息寻找初始聚类中心;此外,基于互信息和散度的比值,确定聚类数目.将所提方法应用于成都某段时间交通热点提取中,并与传统的K-means比较,实验结果表明,所提方法具有更高的聚类精度,提取的热点更符合实际.

Improved K-Means Traffic Data Clustering Based on Mutual Information and Divergence

K-means algorithm is a commonly used clustering algorithm and has been applied to traffic hotspot extraction. However, due to the number of clusters and the subjective setting of the initial clustering center, the traffic hotspots extracted by the existing clustering methods are often difficult to meet the requirements. Based on mutual information and divergence, an improved SK-means algorithm is proposed and applied to traffic hotspot extraction. In the proposed method, an initial clustering center is found based on mutual information between different points. In addition, the number of clusters is determined based on the ratio of mutual information and divergence. The proposed method is applied to the extraction of traffic hotspots in Chengdu for a certain period of time, and compared with the traditional K-means, the experimental results show that the proposed method has higher clustering accuracy and the extracted hotspots are more realistic.