出色的模型挖掘Exceptional Model Mining |
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课程网址: | http://videolectures.net/solomon_knobbe_emm/ |
主讲教师: | Arno Knobbe |
开课单位: | 乌得勒支大学 |
开课时间: | 2008-07-05 |
课程语种: | 英语 |
中文简介: | 在大多数数据库中,有可能在观察到的分布与整个数据库明显不同的情况下识别数据的小分区。在经典的子组发现中,人们考虑了单个名义属性的分布,而例外的子组显示出其值之一的出现令人惊讶地增加。在本演讲中,我将介绍异常模型挖掘(EMM),这是一个允许使用更复杂的目标概念的框架。 EMM不会根据单个目标属性的分布来查找子组,而是会找到适合该子组的模型在某种程度上是例外的子组。我将讨论回归模型和分类模型,并定义质量度量,这些度量确定子组上给定模型的异常程度。我们的框架足够通用,可以应用于多种类型的模型,甚至可以用于关联分析和图形建模等其他范式。 |
课程简介: | In most databases, it is possible to identify small partitions of the data where the observeddistribution is notably different from that of the database as a whole. In classical subgroup discovery, one considers the distribution of a single nominal attribute, and exceptional subgroups show a surprising increase in the occurrence of one of its values. In this talk, I'll introduce Exceptional Model Mining (EMM), a framework that allows for more complicated target concepts. Rather than finding subgroups based on the distribution of a single target attribute, EMM finds subgroups where a model fitted to that subgroup is somehow exceptional. I'll discuss regression as well as classification models, and define quality measures that determine how exceptional a given model on a subgroup is. Our framework is general enough to be applied to many types of models, even from other paradigms such as association analysis and graphical modeling. |
关 键 词: | 数据库; 模型挖掘; 目标概念 |
课程来源: | 视频讲座网 |
最后编审: | 2019-09-22:cwx |
阅读次数: | 39 |