Similarity Measurement Of Data In High-dimensional Spaces
Cai Yi-chao · Shuxue de shijian yu renshi · 2006
Similarity measurement of data in high-dimensional spaces is an important problem in most current research domains such as data mining,information processing searching, etc..After the summarization and analysis of the characteristics of high-dimensional data and existing typical measurement methods,this paper proposes a new measurement approach based on a special grid splitting strategy.In order to illustrate the efficiency of the proposed method in high-dimensional spaces,a quantitative analysis is given in the paper. Experiment indicates that this method is efficacious.