图 书 馆
将近似最佳特征选择与gSpan相结合Combining near-optimal feature selection with gSpan |
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| 课程网址: | http://videolectures.net/mlg08_thoma_cnofs/ |
| 主讲教师: | Marisa Thoma |
| 开课单位: | 慕尼黑大学 |
| 开课时间: | 2008-08-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerized scene analysis, and anomaly detection in program flows. Among the various approaches proposed in the literature, graph classification based on frequent subgraphs is a popular branch: Graphs are represented as (usually binary) vectors, with components indicating whether a graph contains a particular subgraph that is frequent across the dataset. On large graphs, however, one faces the enormous problem that the number of these frequent subgraphs may grow exponentially with the size of the graphs, but only few of them possess enough discriminative power to make them useful for graph classification. Efficient and discriminative feature selection among frequent subgraphs is hence a key challenge for graph mining. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines two central advantages. First, it optimizes a sub modular quality criterion, which means that we can yield a near-optimal solution using greedy feature selection. Second, our sub modular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during frequent subgraph mining. |
| 关 键 词: | 图分类; 频繁子图; 搜索空间 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-02：cjy |
| 阅读次数: | 55 |