The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to th...The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.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The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.
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.
