Rough sets is one important method of data mining. Data mining processes such a great quantity of data inlarge database that the speed of Rough Sets Data Mining Algorithm is critical to Data Mining System. Utilizing n...Rough sets is one important method of data mining. Data mining processes such a great quantity of data inlarge database that the speed of Rough Sets Data Mining Algorithm is critical to Data Mining System. Utilizing net-work computing resources is an effective approach to improve the performance of Data Mining System. This paperproposes the concept of meta-information,which is used to describes the result of Rough Sets Data Mining in informa-tion system,and a meta-information-based method for rule parallel mining. This method decomposes the information-system into a lot of sub-information-system,dispatchs the task of generating meta-information of sub-information-sys-tem to some task performer in the network,and lets them parallel compute meta-information,then synthesizes themeta-information of sub-information-system to the meta-information of information system in the task synthesizer,and finally produces the rule according to the meta-information.展开更多
将基于粗糙集的默认规则挖掘算法(Mining Default Rules Based on Rough Set,MDRBR)用于电力系统短期负荷预测,首先采用基于Gini指标的粗糙集离散化算法对气温、湿度等影响负荷的条件属性进行离散化,同时兼顾了条件属性和决策属性。在...将基于粗糙集的默认规则挖掘算法(Mining Default Rules Based on Rough Set,MDRBR)用于电力系统短期负荷预测,首先采用基于Gini指标的粗糙集离散化算法对气温、湿度等影响负荷的条件属性进行离散化,同时兼顾了条件属性和决策属性。在此基础上,通过计算规则的信赖度和支持度形成不同层次上符合初定阈值的带粗糙集算子的网络规则集,能减少因噪音的影响而产生的多余规则,提高规则产生和实际分类的效率,使所产生的分类规则集大大缩小,提高在使用规则时检索规则的效率。在负荷预测时自上而下逐层搜索规则网直至找出与所给信息相匹配的规则。粗糙集算子反映了规则的重要程度,同时作为选择规则的标准。实际应用表明,该方法能有效去除噪音,提高默认规则的挖掘效率,从而提高负荷预测的精度,具有一定的实用性。展开更多
Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have pr...Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have proposed a new rough set approach based on dominance relation to handle the problems. The concept of dominance matrix is put forward and the dominance function is constructed to compute the minimal decision rules which are more general and applicable than the ones induced by the classical rough set theory. In addition, the methodology of simplification is presented to eliminate the redundancy in the rule set.展开更多
文摘Rough sets is one important method of data mining. Data mining processes such a great quantity of data inlarge database that the speed of Rough Sets Data Mining Algorithm is critical to Data Mining System. Utilizing net-work computing resources is an effective approach to improve the performance of Data Mining System. This paperproposes the concept of meta-information,which is used to describes the result of Rough Sets Data Mining in informa-tion system,and a meta-information-based method for rule parallel mining. This method decomposes the information-system into a lot of sub-information-system,dispatchs the task of generating meta-information of sub-information-sys-tem to some task performer in the network,and lets them parallel compute meta-information,then synthesizes themeta-information of sub-information-system to the meta-information of information system in the task synthesizer,and finally produces the rule according to the meta-information.
文摘将基于粗糙集的默认规则挖掘算法(Mining Default Rules Based on Rough Set,MDRBR)用于电力系统短期负荷预测,首先采用基于Gini指标的粗糙集离散化算法对气温、湿度等影响负荷的条件属性进行离散化,同时兼顾了条件属性和决策属性。在此基础上,通过计算规则的信赖度和支持度形成不同层次上符合初定阈值的带粗糙集算子的网络规则集,能减少因噪音的影响而产生的多余规则,提高规则产生和实际分类的效率,使所产生的分类规则集大大缩小,提高在使用规则时检索规则的效率。在负荷预测时自上而下逐层搜索规则网直至找出与所给信息相匹配的规则。粗糙集算子反映了规则的重要程度,同时作为选择规则的标准。实际应用表明,该方法能有效去除噪音,提高默认规则的挖掘效率,从而提高负荷预测的精度,具有一定的实用性。
文摘Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have proposed a new rough set approach based on dominance relation to handle the problems. The concept of dominance matrix is put forward and the dominance function is constructed to compute the minimal decision rules which are more general and applicable than the ones induced by the classical rough set theory. In addition, the methodology of simplification is presented to eliminate the redundancy in the rule set.
