Integrating Fractal Dimensionality Reduction with Cluster Evolution Tracking
Detecting and tracking of cluster evolution has always been crucial to the stream data mining.While it, in high dimensional stream data environment,becomes more difficult under the interaction between dimensionality reduction and cluster evolution condition.The past has been focus on cluster evolution occurred in the reduced dimensionality space. Dimensionality reduction before the cluster evolution optionjiowever, can not cope with the abrupt changes which are common in stream data. There is the demand of the dimensionality reduction during the process of the cluster evolution,which is the most popular case. In the paper, we pay more attention for the interaction between dimensionality reduction and cluster evolution in the inconstant high dimensional stream data.And on this basis,we propose the adaptive cluster evolution tracking algorithm which integrated the on-line fractal dimensionality reduction technique. Experimental results over a number of real and synthetic data sets show that the method proposed are both effectiveness and efficiency.
Data mining Cluster Evolution Fractal self-adaptive sampling.
Guanghui Yan Yu Xu Xin Shu Xiang Li Minghao Ai Zhicheng Ma
School of Electrical and Computer Engineering Lanzhou Jiaotong University Lanzhou China Gansu Electric Power Information & Communication Centre Lanzhou, China
国际会议
上海
英文
1718-1722
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）