Abstract:
This paper proposes PKDA, an effective method for finding personalized knowledge. By way of deleting the superabundant condition attributes ring upon ring, the interested information is discovered. It gives analyses and comparison of experiment results to illustrate the efficiency and feasibility of the algorithm.
Key words:
Knowledge discovery database(KDD),
Rough Set,
Distinguish matrix,
Reduction
摘要： 利用RS理论和方法提出了个性化知识发现方法——PKDA算法。该算法可以有效地把那些冗余的和用户不感兴趣的信息层层去除，发现用户真正感兴趣的知识。该文给出了实验分析与对比结果，证实了算法的有效性和可行性。
关键词:
数据库知识发现,
Rough set,
分辨矩阵,
约简
MENG Zuqiang; CAI Zixing. Personalized KDD Approach Based on Classification Model[J]. Computer Engineering, 2006, 32(20): 185-187.
蒙祖强;蔡自兴. 基于分类模型的数据库个性化知识发现方法[J]. 计算机工程, 2006, 32(20): 185-187.