自动化系统数据挖掘问题的模拟退火解法

数据挖掘是近年来发展起来的新技术,指挥自动化的辅助决策过程也越来越依靠数据挖掘求解。通过数据挖掘,可以有效地处理海量数据,为正确决策提供支持。模拟退火是一种性能优越的计算智能算法,广泛应用于求解非线性最优化问题。数据挖掘中的数据聚类与模拟退火在本质特性上存在相似,可以将模拟退火应用于数据挖掘。通过蒙特卡罗方法生成随机数据,进行基于模拟退火的聚类分析。算例表明,新方法能够很好地进行数据聚类,具有较好的适用性。

Simulated Annealing Approach for Data Mining in Automation System

Data Mining is a new technology in these days through which the great quantity data can be processed efficiently to support correct decision and the assistant decision procedure in automatism system is more and more depend on Data Mining.Simulated Annealing is a kind of effective compute intelligent algorithm and used to solve nonlinear optimization problems frequently.The Cluster in Data Mining and Simulated Annealing is same in essence character,so Simulate Annealing can be used to solve Cluster in Data Mining.The experiment data is got by Montecarlo method and be solved by cluster based on Simulated Annealing.The sample demonstrates that this new method can work effectively and has good applicability.