An Efficient Chernoff Faces Clustering Algorithms

摘要：

Chernoff faces are an excellent tool in the visualization of multivariate data. It is drawn by assigning the data dimension to the facial feature. Each face is human like and the user identifies the clusters from the faces. To improve the shortcomings of Chernoff faces clustering, we present a simple and efficient implementation of Chernoff faces with the integration of principle component analysis(PCA) and k-means algorithm. The two-stage procedure-first using k-means algorithm to produce the cluster prototypes that are then used to reclassify all data or the partial special data using the improved Chernoff faces based on PCA-is found to reduce the subjective judgment time of classify data and to perform well when compared with Chernoff faces clustering.

作者: Jinjia Wang Jialin Song Xin Li Wenxue Hong

作者单位: Yanshan University,Qinhuadao, Hebei province, P.R. China,066004

会议类型: 国际会议

会议名称: 第三届国际脉冲动力系统及应用学术会议

会议地点: 青岛

会议语种:英文

在线出版日期: 2006-07-21（万方平台首次上网日期，不代表论文的发表时间）