基于复合粒子群的数据分类方法

Classifying algorithm based on compound particle swarm optimization

分类是数据挖掘中的一个重要任务。当前许多分类算法一般要求处理离散属性数据,提出了一种新的基于复合粒子群算法,它能对含有连续属性和离散属性值的混合数据进行分类。为提高分类正确率和效率,对基本粒子群采用复合结构编码,通过粒子群算法得到连续属性离散化后的候选分割点并分类,将混合数据分类问题转化为0-1组合优化问题。实验结果证明,该算法有很好的分类效果,而且具有较快的收敛速度。

Classification is important in data mining.Many classifying algorithms often deal with some discrete data,so the paper puts forward a new classifying algorithm based on compound particle swarm optimization,it can classify the data with the continuous and discrete attribute.In order to advance the classification validity and the effficiency,the paper codes with the compound particle structure,and gets the discrete data by dispersing the continuous attribute data,then classifies them by the particle swarm optimization,which translate the problem of the mix-data classification into the combination optimize problem with 0-1.The experiment results prove that the algorithm has a good effect and speediness.