基于量子行为粒子群优化的软子空间聚类算法

Soft Subspace Clustering Algorithm Based on Quantum-Behaved Particle Swarm Optimization

针对软子空间聚类算法搜寻聚类中心点容易陷入局部最优的缺点,提出在软子空间聚类框架下,结合量子行为粒子群优化(QPSO)和梯度下降法优化软子空间聚类目标函数的模糊聚类算法.根据QPSO全局寻优的特点,求解子空间中全局最优中心点,利用梯度下降法收敛速度快的特点,求解样本点的模糊权重和隶属度矩阵,最终获取样本点的最优聚类结果.在UCI数据集上的实验表明,文中算法可提高聚类精度和聚类结果的稳定性.

Soft subspace clustering algorithm frequently falls into local optimum during searching clustering center point. A fuzzy clustering algorithm is proposed based on the framework of soft subspace clustering, and it integrates quantum-behaved particle swarm optimization （QPSO） algorithm into gradient descent method to optimize the objective function in soft subspace clustering. By the characteristic of searching global optimum in the QPSO algorithm, global optimal center points are solved in the subspace, and then by the high convergence speed of the gradient descent method, fuzzy weights and membership degree matrices of sample points can be obtained. Finally, the optimal clustering results of sample points are obtained. Experiment is carried out on UCI dataset and the results demonstrate the improvement in accuracy as well as the stability of the clustering results of the proposed method.