作　　者： ; ; ;

机构地区： 华南理工大学电力学院

出　　处： 《华南理工大学学报（自然科学版）》 2009年第7期100-105,共6页

摘　　要： 文中提出了一种改进的知识发现算法.针对基于留数分析和递归切割的模式发现算法的不足,在样本空间分割时,考虑到不同属性对知识发现的不同贡献,采取不同的离散化标准;在模式判别准则方面,改良后的算法随切割后产生的子空间数量动态调整模式判别准则.在人造数据集和电力系统安全稳定评估中的应用结果验证了算法改进思路的合理性与有效性.与原算法相比,改良后算法的知识发现效率更高、应用范围更广. A modified knowledge discovery algorithm is proposed in this paper. In view of the limitations of the original pattern discovery algorithm based on residual analysis and recursive partitioning, different discretization criteria are adopted during the partitioning of sample space because different attributes have different contributions to pattern discovery. The proposed algorithm automatically adjusts the pattern-classifying criterion according to the subspace number. The application results on synthetic data set and power system stability assessment show that the proposed algorithm is rational and effective, and that it is of higher knowledge discovery efficiency and wider application, as compared with the original algorithm.

关 键 词： 知识发现 模式发现 离散化 电网 安全评估

领　　域： [自动化与计算机技术] [自动化与计算机技术] [电气工程]