In this paper, a novel Discrete Particle Swarm Optimization Algorithm (DPSOA) for data clustering has been proposed. The particle positions and velocities are defined in a discrete form. The DPSOA algorithm uses of a simple probability approach

分数阶傅里叶变换计算方面,用MATLAB实现动态聚类或迭代自组织数据分析，最大似然（ML）准则和最大后验概率（MAP）准则。- Fractional Fourier transform computing, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criter