Exceptional Preferences Mining
Exceptional Preferences Mining
复制标题
特殊偏好挖矿
DOI：
10.1007/978-3-319-46307-0_1
复制
发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Knobbe
中科院分区：
文献类型：
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作者：
C. Sá;W. Duivesteijn;Carlos Soares;A. Knobbe
例外偏好挖掘是数据挖掘的两个子领域：局部模式挖掘和偏好学习的交叉。EPM可以被看作是一种局部模式挖掘任务，它发现标签子集之间的偏好关系明显偏离规范的观察子集；子组发现的变体，将排名作为(复杂的)目标概念。我们采用了三种质量衡量标准来突出具有特殊偏好的子组，其中，什么是“特殊”的侧重点随着质量衡量标准的不同而不同：第一种是衡量特殊的整体排名行为，第二种是表明某个特定的标签是否从其他标签中脱颖而出，第三种是强调具有不寻常的成对标签排名行为的子组。作为概念验证，我们探索了五个数据集。结果证实，新的任务EPM能够传递有趣的知识。结果还说明了偏好矩阵中偏好的可视化如何有助于解释特殊的偏好子组。
Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups.