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，与单个规则相比，结构化规则代表了更多的相关信息，因此有望更频繁地吸引用户的兴趣。在这个项目中，我们假设发现结果的候选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，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。首先，选取动作规则，每个动作规则提出可操作属性值的变化作为发现结果的表示，发明了一种发现方法，并将其作为原型系统实现。该系统是一种通用的数据挖掘方法，能够从驻留在磁盘上的海量数据中发现具有高可实现性的坏类转化为好类的动作规则。使用朴素贝叶斯分类器评估每个动作或更多规则的可实现性，该分类器从两个类的示例集中学习。我们通过使用包括美国人口普查在内的数据集的实验证明了所提出方法的有效性。其次，我们发明了结构化数据挖掘，在不指定领域知识种类的情况下，通过信息压缩发现看似自然的结构化知识的部分决策列表。我们将该发明实现为一种扩展的最小描述长度原理，该原理有助于通过考虑领域知识来发现解释部分示例空间的知识，以及该原理的搜索方法。该搜索方法尝试了三种启发式搜索方法，并返回描述长度最短的假设。我们已经将其作为原型系统实现，并从其评估中发现了许多有趣的结果，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，这些方法可以作为所提出方法的基础。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