基于量化误差与分形理论的高计算效率无监督聚类研究.

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 modeling of the quantization error for data set, got the rate-distortion curve based on the space structure of the data set. Then, it computed the efficient dimensionality of the data set by estimation of the rate distortion curve. Lastly, it obtained the optimal clustering 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. [ABSTRACT FROM AUTHOR]