【摘要】 传统近邻传播聚类算法(affinity propagation,AP)的偏向参数与数据集相关,若不根据数据集作优化,将导致算法聚类精度低。为此提出一种量子近邻传播聚类算法,首先用量子叠加态编码偏向参数,再用量子旋转门搜索量子叠加态,找出近优的偏向参数供AP算法聚类,达到自适应优化AP偏向参数的目的。在UCI数据集上的实验结果表明,本文提出的算法聚类精度比同类算法更优,计算时间和迭代次数亦优于传统AP算法,引入量子计算优化近邻传播聚类算法的思路是可行的。更多还原

【Abstract】 The preference of the affinity propagation( AP) clustering algorithm is related to the data sets. If the algorithm is not optimized according to the data sets,the clustering accuracy of the algorithm will be reduced. A quantum affinity propagation clustering algorithm is proposed in this paper. Firstly,the preference parameter is encoded by the quantum superposition state. Secondly,the superposition state of the quantum is searched by the quantum rotate gate,with the purpose of finding out the approximately optimal preference value for AP algorithm clustering. By that method,the AP preference parameter can be optimized adaptively. Experimental results on the UCI data sets show that the proposed algorithm gets better clustering accuracy against several other similar algorithms,and the computation time and the number of iterations are superior to the traditional AP algorithm. Therefore,the idea that quantum computation is introduced in to optimize the affinity propagation clustering algorithm is feasible.更多还原