An autonomous method on tracking stream data cluster evolution
Stream data can often show abrupt changes over time. It is very critical to analyze and predict the trends autonomously on time in the continuous, high speed and variable stream data environment. In this paper, we discuss the Autonomous Real-time Clustering Evolution Tracking algorithm which integrate the fractal cluster technique, selfadaptive sampling technique with the restriction of computing resource and the requirement of processing speed, and can discriminate the cluster evolution of stream data on time autonomously. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness and efficiency provided by our approach.
Data mining Cluster Evolution Fractal selfadaptive sampling
Guanghui Yan Zhicheng Ma Shaoling He Yun Liu Xiaohui Dong
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China Gansu Electric Power Information & Communimtion Centre, Lanzhou, China
国际会议
厦门
英文
461-465
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）