Research on Dynamic Data Streams Clustering Algorithm-Pdstream based on PCA and Density

摘要：

The research on data streams clustering has become a focus in the field of data streams mining. Because the number of data streams is too large, and CPU of the computer has limited memory and time, its difficult to carry out clustering quickly and effectively. For that problem, we design an improved clustering algorithm for dynamic data streams based on principal component analysis and density. The PDStream algorithm effectively overcomes the shortcomings of the STREAM algorithm controlled by historical data and the CluStream algorithm is difficult to describe non-spherical and out old data, resulting in huge amount of data, In the course of the experiment, we compare with the STREAM algorithm, the PDStream algorithm shows the superiority of handling mass data and the characteristics of high-quality clustering.

关键词： data streams principal component analysis density sliding window

作者: Mei Zheng Chunhua Ju Zhang Rui

作者单位: Coliege of Computer Science & Information, Zhejiang Gongshang University, Hangzhou, China Center for Studies of Modern Business Zhejiang Gongshang University, Hangzhou, China

会议类型: 国际会议

会议名称: 2010 International Conference on Advanced Mechanical Engineering(2010年先进机械工程国际学术会议 AME 2010)

会议地点: 洛阳

会议语种:英文

页码: 108-112

在线出版日期: 2010-09-04（万方平台首次上网日期，不代表论文的发表时间）