计算机应用研究2016，Vol.33Issue(10)：2919-2922,4.DOI:10.3969/j.issn.1001-3695.2016.10.009
基于量化误差与分形理论的高计算效率无监督聚类研究
Quantization error and fractal theory based high computation efficiency unsupervised clustering algorithm
摘要
Abstract
The existing vector clustering algorithm need to learn a lot of complex data in order to get a good performance for clustering,and it does not have good performance for big data.This paper proposed a quantization error and fractal theory based high computation efficiency unsupervised clustering algorithm to solve that problem.Firstly,it constructed a parametric model-ing of the quantization error for data set,got the rate-distortion curve based on the space structure of the data set.Then,it com-puted the efficient dimensionality of the data set by estimation of the rate distortion curve.Lastly,it obtained the optimal cluste-ring number of the target data set by fractal theory.Experiments result shows that the proposed quantization error modeling can estimate the quantization error very well and the proposed algorithm has better performance in search the best clustering number and computation efficiency than the existing vector clustering algorithm.关键词
分形理论/量化误差/率失真曲线/无监督聚类/多维数据Key words
fractal theory/quantization error/rate distortion curve/unsupervised clustering/multidimensional data分类
信息技术与安全科学引用本文复制引用
胡国生,杨海涛..基于量化误差与分形理论的高计算效率无监督聚类研究[J].计算机应用研究,2016,33(10):2919-2922,4.基金项目
浙江省自然科学基金资助项目（Y1090416）；浙江省自然科学基金资助项目 （）
