Improved Hierarchical K-means Clustering Algorithm without Iteration Based on Distance Measurement

摘要：

　　Hierarchical K-means has got rapid development and wide application because of combining the advantage of high accuracy of hierarchical algorithm and fast convergence of K-means in recent years.Traditional HK clustering algorithm first determines to the initial cluster centers and the number of clusters by agglomerative algorithm, but agglomerative algorithm merges two data objects of minimum distance in dataset every time.Hence, its time complexity can not be acceptable for analyzing huge dataset.In view of the above problem of the traditional HK, this paper proposes a new clustering algorithm iHK.Its basic idea is that the each layer of the N data objects constructs 「N/2」 clusters by running K-means algorithm, and the mean vector of each cluster is used as the input of the next layer.iHK algorithm is tested on many different types of dataset and excellent experimental results are got.

关键词： basic K-means traditional HK iHK Clustering Algorithm

作者: Wenhua Liu Yongquan Liang Jiancong Fan Zheng Feng Yuhao Cai

作者单位: College of Information Science and Engineering,Shandong University of Science and Technology,Qingdao City, 266590, China

会议类型: 国际会议

会议名称: 8th International Conference on Intelligent Information Processing(2014年IFIP智能信息处理国际会议)

会议地点: 杭州

会议语种:英文

页码: 38-46

在线出版日期: 2014-10-01（万方平台首次上网日期，不代表论文的发表时间）