两阶段近邻传播半监督聚类算法

近邻传播聚类算法(affinity propagation, AP)受偏向参数影响较大,很难确定最优聚类所需的参数。设计了两阶段近邻传播半监督聚类算法(two-stage semi supervised clustering algorithm based on affinity propagation, 2SAP),在整个数据集上运行半监督近邻传播算法(semi-supervised clustering based on affinity propagation, SAP),得出类代表点集合,在类代表点集合上运行SAP算法得出结果。在实际数据集上进行实验,结果证实:与算法SAP和并行近邻传播半监督聚类算法(parallel computation of semi-supervised clustering algorithm based on affinity propagation,PSAP)相比,2SAP算法的CRI和FCRI值较高,而相应的离散系数较小,说明2SAP受偏向参数的影响较小。

Two-stage semi-supervised clustering algorithm based on affinity propagation

The affinity propagation clustering algorithm(AP) is sensitive to the preference value,and it is difficult to find the optimal preference value.2SAP,a two-stage semi-supervised clustering algorithm based on AP,was proposed to overcome this limitation.Semi-supervised clustering based on affinity propagation(SAP) was used to cluster the whole dataset and obtain the exemplar set,and then the SAP was used again to cluster the exemplar set to find the final clusters.Experimental results on real data sets showed that the 2SAP was better than SAP and PSAP in terms of CRI and FCRI,and the lower coefficients of dispersion illustrated that 2SAP was less sensitive to the preference value.