复合形退火的随机聚类算法

Random optimal clustering algorithm based on complex annealing method

为了解决传统聚类算法易陷入局部最优解的问题,提出了一种复合形退火的随机聚类算法。该方法通过在聚类过程中设置退火准则,并且将退火过程中的生成复合形部分引入随机化的复合形节点,从而在加速收敛的过程中实现了较低的算法复杂度。理论分析及仿真实验证明,该方法的聚类效果好于传统的K-均值聚类方法,并且计算复杂度比目前基于人工智能的方法低。

In order to solve the problem that the traditional clustering algorithm is apt to fall into local optimal solution,this paper presented a novel algorithm.First it introduced the main principle of the complex method and used an improved one to K-mean optimal clustering,this paper educed a series of formulation and gave the realization process of the algorithm later.Compared to other algorithms,the results by exemplification show that this algorithm is not only correct and feasible,but highly effective and steady,and has a wide area of application.