基于分类的加速EM缺失数据填充算法

Accelerating EM Missing Data Filling Algorithm Based on the Clustering

在数据挖掘的整个过程中,EM算法因其数值计算的稳定性、实现上的简单性,可靠的全局收敛性,被广泛应用于处理数据不完整问题。针对EM算法收敛速度慢,算法高度依赖初始值的选择,使用KNN算法的分类结果作为EM算法的初始使用范围,KNN算法根据挖掘目的的不同选择不同的特性,然后利用增量式EM(IEM)算法按E步M步迭代反复求精,快速有效地得出填充缺失数据的最优值;该算法大大加快了收敛速度,加强了聚类的稳定性,数据填充效果显著。

In the whole process of data mining, the EM algorithm is widely applied to dealing with incomplete data for its numerical stability, simplicity of implementation, reliable global convergence. the main disadvantage of the EM is slow convergence speed, the algorithm is highly dependent on the initial value of the option, In this paper, the clustering results use kNN Classification as the initial scope of EM algorithm, according to the different choice of different characteristics of mining purposes, then use incremental EM algorithm （IEM） step by step EM iterative refinement repeatedly, it obtains the optimal value of filling missing data quickly and efficiently, it is concluded that the optimal value of filling missing data experimental results show that the algorithm of this paper to speed up the convergence rate, strengthened the stability of clustering, data filling effect is remarkable.