关键词: 近邻传播, 多维空间, 属性权重, 空间分布

Abstract: In view of the problem that the affinity propagation algorithm is not suitable for dealing with multiple scales and arbitrary shape data, this paper presents an MSAAP (multidimensional similarity adaptive affinity propagation) algorithm based on multidimensional spatial transform. First, the attribute weight of the data set is calculated by the entropy method. Second, this paper puts forward a new method of computing the similarity matrix based on attribute weights. Last, this paper divides the spatial space of the sample points into several spatial blocks according to the priority of the attribute weights, calculates the degree of appeal and the degree of attribution of the space block, and then adjusts the spatial distribution of particles. This paper uses 13 UCI data sets and 3 face databases to do comparative experiments, and then from the accuracy rate, the algorithm time, the number of clusters to analyze the 3 dimensions. Finally, the experimental results show that the MSAAP clustering effect is better.

Key words: affinity propagation, multidimensional space, attribute weight, spatial distribution