图 书 馆
在大型数据集中挖掘，索引和搜索图形Mining, Indexing, and Searching Graphs in Large Data Sets |
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| 课程网址: | http://videolectures.net/mlg07_han_miasg/ |
| 主讲教师: | Jiawei Han |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2007-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Recent research on pattern discovery has progressed from mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide applications in Web, social network analysis, and bioinformatics. However, mining and searching large graphs in graph databases is challenging due to the presence of an exponential number of frequent subgraphs. In this talk, we present our recent progress on developing efficient and scalable methods for mining and searching of graphs in large databases. We introduce gSpan and CloseGraph, two efficient methods for mining frequent graph patterns in graph databases. Then we introduce constraint-based graph mining methods. Further, we introduce a graph indexing method, gIndex, and a graph approximate searching method, grafil, both taking advantages of frequent graph mining to construct a compact but highly effective graph index and perform similarity search with such indexing structures. These methods not only facilitate mining and querying graph patterns in massive datasets but also claim broad applications in other fields, including DB/OS systems and software engineering. |
| 关 键 词: | 结构化模式; 频繁子图; 图挖掘方法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-15：heyf |
| 阅读次数: | 39 |