图 书 馆
在存在对称性的情况下改进频繁的子图挖掘Improving frequent subgraph mining in the presence of symmetry |
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| 课程网址: | http://videolectures.net/mlg07_desrosiers_ifsm/ |
| 主讲教师: | Christian Desrosiers |
| 开课单位: | 蒙特利尔理工学院 |
| 开课时间: | 2007-09-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The difficulty of the frequent subgraph mining problem arises from the tasks of enumerating the subgraphs and calculating their support in the dataset. If the dataset graphs have additional information in the form of labels, these problems can be solved quite easily. However, if the dataset graphs are unlabeled or only have a few labels, then the complexity of these problems greatly reduces the number and sizes of the dataset graphs that can be managed. Thus far, researchers working on the frequent subgraph mining problem have given little attention to such datasets, and current algorithms tend to do poorly on them. Yet, there are many applications which deal with this type of data, mainly in the fields of compute vision where the data is structured as 2D or 3D meshes [8], or communication/transportation networks where the information is mostly topological. |
| 关 键 词: | 频繁子图; 数据集图表; 计算数据结构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-06-30：cjy |
| 阅读次数: | 41 |