基于加权Bayes分类算法的不完备信息系统数据挖掘研究

Reseach for Data Mining of Incomplete Information Systems Based on Weighted Naive Classification Algorithm

基于相似粗集理论模型,对加权朴素Bayes算法进行了扩展,同时改进了传统不完备信息系统中缺失信息的弥补方法,并由此提出了基于不完备信息系统的加权Bayes分类算法,阐述了其对于不完备系统数据挖掘的重要意义,通过计算机仿真实验验证了该方法的有效性。

Weighted Na？ve Classification Algorithm is extended based on Comparability Rough sets theory. The original recuperation method of lost information in Incomplete Information Systems is improved too. Weighted Na？ve Classification Algorithm based on Incomplete Information Systems is developed, and its slgmficance for data mining is also set forth. Simulation results on a variety of data set illustrate the efficiency of this new algorithm.