An enhanced cascading failure model integrating data mining technique is proposed in this paper.In order to better simulate the process of cascading failure propagation and further analyze the relationship between fai...An enhanced cascading failure model integrating data mining technique is proposed in this paper.In order to better simulate the process of cascading failure propagation and further analyze the relationship between failure chains,in view of a basic framework of cascading failure described in this paper,some significant improvements in emerging prevention and control measures,the subsequent failure search strategy as well as the statistical analysis for the failure chains are made elaborately.Especially,a sequential pattern mining model is employed to find out the association pertinent to the obtained failure chains.In addition,a cluster analysis model is applied to evaluate the relationship between the intermediate data and the consequence of obtained failure chain,which can provide the prediction in potential propagation path of cascading failure to reduce the risk of catastrophic events.Finally,the case studies are conducted on the IEEE 10-machine-39-bus test system as benchmark to demonstrate the validity and effectiveness of the proposed enhanced cascading failure model.Some preliminary concluding remarks and comments are drawn.展开更多
基于模式的贝叶斯分类模型是解决数据挖掘领域分类问题的一种有效方法.然而,大多数基于模式的贝叶斯分类器只考虑模式在目标类数据集中的支持度,而忽略了模式在对立类数据集合中的支持度.此外,对于高速动态变化的无限数据流环境,在静态...基于模式的贝叶斯分类模型是解决数据挖掘领域分类问题的一种有效方法.然而,大多数基于模式的贝叶斯分类器只考虑模式在目标类数据集中的支持度,而忽略了模式在对立类数据集合中的支持度.此外,对于高速动态变化的无限数据流环境,在静态数据集下的基于模式的贝叶斯分类器就不能适用.为了解决这些问题,提出了基于显露模式的数据流贝叶斯分类模型EPDS(Bayesian classifier algorithm based on emerging pattern for data stream).该模型使用一个简单的混合森林结构来维护内存中事务的项集,并采用一种快速的模式抽取机制来提高算法速度.EPDS采用半懒惰式学习策略持续更新显露模式,并为待分类事务在每个类下建立局部分类模型.大量实验结果表明,该算法比其他数据流分类模型有较高的准确度.展开更多
基金the National Basic Research Program of China,973 program(2013CB228203).
文摘An enhanced cascading failure model integrating data mining technique is proposed in this paper.In order to better simulate the process of cascading failure propagation and further analyze the relationship between failure chains,in view of a basic framework of cascading failure described in this paper,some significant improvements in emerging prevention and control measures,the subsequent failure search strategy as well as the statistical analysis for the failure chains are made elaborately.Especially,a sequential pattern mining model is employed to find out the association pertinent to the obtained failure chains.In addition,a cluster analysis model is applied to evaluate the relationship between the intermediate data and the consequence of obtained failure chain,which can provide the prediction in potential propagation path of cascading failure to reduce the risk of catastrophic events.Finally,the case studies are conducted on the IEEE 10-machine-39-bus test system as benchmark to demonstrate the validity and effectiveness of the proposed enhanced cascading failure model.Some preliminary concluding remarks and comments are drawn.
文摘基于模式的贝叶斯分类模型是解决数据挖掘领域分类问题的一种有效方法.然而,大多数基于模式的贝叶斯分类器只考虑模式在目标类数据集中的支持度,而忽略了模式在对立类数据集合中的支持度.此外,对于高速动态变化的无限数据流环境,在静态数据集下的基于模式的贝叶斯分类器就不能适用.为了解决这些问题,提出了基于显露模式的数据流贝叶斯分类模型EPDS(Bayesian classifier algorithm based on emerging pattern for data stream).该模型使用一个简单的混合森林结构来维护内存中事务的项集,并采用一种快速的模式抽取机制来提高算法速度.EPDS采用半懒惰式学习策略持续更新显露模式,并为待分类事务在每个类下建立局部分类模型.大量实验结果表明,该算法比其他数据流分类模型有较高的准确度.
