Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description fram...Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.展开更多
The amount of data for decision making has increased tremendously in the age of the digital economy. Decision makers who fail to proficiently manipulate the data produced may make incorrect decisions and therefore har...The amount of data for decision making has increased tremendously in the age of the digital economy. Decision makers who fail to proficiently manipulate the data produced may make incorrect decisions and therefore harm their business. Thus, the task of extracting and classifying the useful information efficiently and effectively from huge amounts of computational data is of special importance. In this paper, we consider that the attributes of data could be both crisp and fuzzy. By examining the suitable partial data, segments with different classes are formed, then a multithreaded computation is performed to generate crisp rules (if possible), and finally, the fuzzy partition technique is employed to deal with the fuzzy attributes for classification. The rules generated in classifying the overall data can be used to gain more knowledge from the data collected.展开更多
Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. ...Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. So it is believable that integrating the knowledge embedded in data and those possessed by experts can lead to a superior modeling approach. Aiming at the classification problems, a novel integrated knowledge-based modeling methodology, oriented by experts and driven by data, is proposed. It starts from experts identifying modeling parameters, and then the input space is partitioned followed by fuzzification. Afterwards, single rules are generated and then aggregated to form a rule base, on which a fuzzy inference mechanism is proposed. The experts are allowed to make necessary changes on the rule base to improve the model accuracy. A real-world application, welding fault diagnosis, is presented to demonstrate the effectiveness of the methodology.展开更多
Mining knowledge from database has been thought as a key research issue in database system. Great mterest has been paid in data mining by researchers in different fields. In this paper,data mining techniques are intro...Mining knowledge from database has been thought as a key research issue in database system. Great mterest has been paid in data mining by researchers in different fields. In this paper,data mining techniques are introduced broadly including its definition,purpose,characteristic, principal processes and classifications. As an example,the studies on the mining association rules are illustrated. At last,some data mining prototypes are provided and several research trends on the data mining are discussed.展开更多
在KDD(knowledge discovery in database)中,对所发现的知识进行评价是一个很重要的环节.提出了一种针对KDD中因果关联规则的自动评价方法.该评价方法采用了全新的、有效的知识表示方法(语言场和语言值结构)和推理机制(因果关系定性推...在KDD(knowledge discovery in database)中,对所发现的知识进行评价是一个很重要的环节.提出了一种针对KDD中因果关联规则的自动评价方法.该评价方法采用了全新的、有效的知识表示方法(语言场和语言值结构)和推理机制(因果关系定性推理机制),并且具有通用性和交互性的特征.给出了此评价方法的理论依据和构造过程,并提供了相应的算法.通过对具体实例的运行检验,证明了此评价方法的有效性.通过与相关工作的比较,证明了其先进性.展开更多
文摘Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.
文摘The amount of data for decision making has increased tremendously in the age of the digital economy. Decision makers who fail to proficiently manipulate the data produced may make incorrect decisions and therefore harm their business. Thus, the task of extracting and classifying the useful information efficiently and effectively from huge amounts of computational data is of special importance. In this paper, we consider that the attributes of data could be both crisp and fuzzy. By examining the suitable partial data, segments with different classes are formed, then a multithreaded computation is performed to generate crisp rules (if possible), and finally, the fuzzy partition technique is employed to deal with the fuzzy attributes for classification. The rules generated in classifying the overall data can be used to gain more knowledge from the data collected.
基金partially supported by the Overseas Research Scholar Fund from Zhejiang University of Technology.
文摘Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. So it is believable that integrating the knowledge embedded in data and those possessed by experts can lead to a superior modeling approach. Aiming at the classification problems, a novel integrated knowledge-based modeling methodology, oriented by experts and driven by data, is proposed. It starts from experts identifying modeling parameters, and then the input space is partitioned followed by fuzzification. Afterwards, single rules are generated and then aggregated to form a rule base, on which a fuzzy inference mechanism is proposed. The experts are allowed to make necessary changes on the rule base to improve the model accuracy. A real-world application, welding fault diagnosis, is presented to demonstrate the effectiveness of the methodology.
文摘Mining knowledge from database has been thought as a key research issue in database system. Great mterest has been paid in data mining by researchers in different fields. In this paper,data mining techniques are introduced broadly including its definition,purpose,characteristic, principal processes and classifications. As an example,the studies on the mining association rules are illustrated. At last,some data mining prototypes are provided and several research trends on the data mining are discussed.
文摘在KDD(knowledge discovery in database)中,对所发现的知识进行评价是一个很重要的环节.提出了一种针对KDD中因果关联规则的自动评价方法.该评价方法采用了全新的、有效的知识表示方法(语言场和语言值结构)和推理机制(因果关系定性推理机制),并且具有通用性和交互性的特征.给出了此评价方法的理论依据和构造过程,并提供了相应的算法.通过对具体实例的运行检验,证明了此评价方法的有效性.通过与相关工作的比较,证明了其先进性.
