Mining, Indexing, and Searching Graphs in Large Data Sets
published: Sept. 6, 2007, recorded: August 2007, views: 13996
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !