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大型复杂数据中的非冗余子群发现Non-Redundant Subgroup Discovery in Large and Complex Data |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_van_leeuwen_data/ |
| 主讲教师: | Matthijs van Leeuwen |
| 开课单位: | 荷兰乌得勒支大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Large and complex data is challenging for most existing discovery algorithms, for several reasons. First of all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible. Second, many variants of essentially the same pattern exist, due to (numeric) attributes of high cardinality, correlated attributes, and so on. This causes top-k mining algorithms to return highly redundant result sets, while ignoring many potentially interesting results. These problems are particularly apparent with Subgroup Discovery and its generalisation, Exceptional Model Mining. To address this, we introduce subgroup set mining: one should not consider individual subgroups, but sets of subgroups. We consider three degrees of redundancy, and propose corresponding heuristic selection strategies in order to eliminate redundancy. By incorporating these strategies in a beam search, the balance between exploration and exploitation is improved. Experiments clearly show that the proposed methods result in much more diverse subgroup sets than traditional Subgroup Discovery methods. |
| 关 键 词: | 模式挖掘; 启发式选择策略; 多样化子群集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-17：cwx |
| 阅读次数: | 69 |