目标函数和子空间搜索策略决定软子空间聚类算法的性能，而聚类有效性分析是衡量其性能的主要指标。针对子空间聚类性能，提出基于粒子群优化的软子空间聚类算法(SC-WPSO)。首先，利用 K 均值类型框架，结合类间分散度和特征权重，提出模糊加权软子空间聚类目标函数。然后，为跳出局部最优，将带惯性权重的粒子群算法作为子空间的搜索策略。最后，根据提出的聚类有效性函数，选取最佳聚类数目。在数据集上的实验证实 SC-PSO能提高聚类准确度，同时自动确定最佳聚类数目。%The performance of soft subspace clustering depends on the objective function and subspace search strategy, and cluster validity analysis is the main indicator of its performance. Aiming at the subspace clustering performance, a soft subspace clustering algorithm based on particle swarm optimization (SC-PSO) is proposed. Firstly, combining inter-cluster separation with feature weight based on K means-type clustering framework, a fuzzy weighting soft subspace objective function is designed. Then, particle swarm optimization with inertia weight is used as a subspace search strategy to jump out of the local optimum. Finally, the optimal cluster number is selected by the proposed cluster validity function. The experimental results demonstrate that SC-PSO improves the clustering accuracy and automatically determines the optimal cluster number.
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