Structured Data Mining System which Considers Interactions of Structured Rules

结构化规则被定义为相互关联的规则，比单个规则表示更多的相关信息，因此期望更频繁地吸引用户的兴趣。在这个项目中，我们假设作为发现结果候选的结构化规则集和用户给出的结构化规则集在发现过程中相互影响，并且我们建立了一个通用的数据挖掘系统，通过考虑这种相互影响来限制有趣的发现结果。我们已经选择了动作规则，其中每个动作规则提出了可动作属性值的变化作为发现结果的表示，发明了发现方法，并将其实现为原型系统。该系统是一种通用的数据挖掘方法，它能从磁盘存储的海量数据中发现具有高可信度的行为规则，将坏类转化为好类。每个动作r的可重复性 ...更多信息 使用朴素贝叶斯分类器来评估ULE，该分类器是从两个类的示例集合中学习的。我们已经证明了所提出的方法的有效性，实验采用的数据集，包括美国。S.第二，我们发明了结构化数据挖掘，它发现了一个部分决策列表，这似乎是自然的结构化知识的基础上，信息压缩，而不指定领域知识的种类。我们已经将本发明实现为扩展的最小描述长度原则和用于该原则的搜索方法，该原则通过考虑领域知识来帮助发现解释示例空间的一部分的知识。搜索方法尝试了三种启发式搜索方法，并返回具有最短描述长度的假设。我们已经实现了它作为一个原型系统，并发现，从它的评估，许多有趣的结果，包括对噪声的高鲁棒性。我们还开发了相关的数据挖掘方法和搜索方法，可以作为所提出的方法的基础。少

Structured rules, which are defined as mutually related rules, represent more related information th an a single rule and thus are expected to attract the interest of the user more frequently. In this project, we assume that the set of the structured rules which are candidates of the outcome of the discovery and the set of the structured rules given by the user mutually influence each other in a discovery process, and we have established a general data mining system which restricts interesting discovery outcomes by considering such mutual influence.Firstly, we have selected action rules each of which proposes changes of the values of actionable at tributes as the representation of the discovery outcome, invented a discovery method, and implemented it as a prototype system. This system is a general data mining method which discovers action rules which exhibit high achievability for changing a bad class into a good class from disk-resident massive data. The achievability of each action r … More ule is evaluated using the Naive Bayes classifier which is learnt from the sets of examples of both classes. We have demonstrated the effectiveness of the proposed method by experiments which employ data sets including the U. S. Census.Secondly, we have invented structured data mining which discovers a partial decision list which seems natural as structured knowledge based on information compression without specifying kinds of domain knowledge. We have realized this invention as an extended Minimum Description Length principle which helps to discover knowledge which explains a part of the example space by considering domain knowledge, and a search method for the principle. The search method tries three kinds of heuristic search methods and returns the hypothesis that has the shortest description length. We have implemented it as a prototype system and found, from its evaluation, many interesting results including high robustness against noise. We have also developed related data mining methods and search methods which may serve as bases of the proposed methods. Less