图 书 馆
发现可靠的近似函数依赖关系Discovering Reliable Approximate Functional Dependencies |
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| 课程网址: | http://videolectures.net/kdd2017_mandros_functional_dependencies/ |
| 主讲教师: | Panagiotis Mandros |
| 开课单位: | 马克斯普朗克信息学研究所 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or α-approximate top-k dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity. |
| 关 键 词: | 函数依赖关系; 数据挖掘; 数据科学 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-12-26：wujk |
| 最后编审: | 2023-12-26：wujk |
| 阅读次数: | 23 |