A new clustering method suitable for large scale data
Xu Yin, Hong Xingyong, Zhou Wenjiang, Wang Lunwen, Ling Zhang, Ying Tan · 2008
In this paper, constructive neural networks (i.e. CNN) are used to cluster large-scale patterns, and the optimum granularity is chosen by quotient space granularity analysis method. This method not only makes good use of the characteristic of CNN in reducing the computing complexity, but also takes the advantage of quotient space theory in choosing the optimum granularity. So it can cluster large-scale and complicated data effectively. The results of the experiments show the validity of this method.