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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gudes, E. Shimony, S.E. Vanetik, N. |
| Copyright Year | 1989 |
| Abstract | Whereas data mining in structured data focuses on frequent data values, in semistructured and graph data mining, the issue is frequent labels and common specific topologies. The structure of the data is just as important as its content. We study the problem of discovering typical patterns of graph data, a task made difficult because of the complexity of required subtasks, especially subgraph isomorphism. In this paper, we propose a new apriori-based algorithm for mining graph data, where the basic building blocks are relatively large, disjoint paths. The algorithm is proven to be sound and complete. Empirical evidence shows practical advantages of our approach for certain categories of graphs |
| Sponsorship | IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society |
| Starting Page | 1441 |
| Ending Page | 1456 |
| Page Count | 16 |
| File Size | 2801037 |
| File Format | |
| ISSN | 10414347 |
| Volume Number | 18 |
| Issue Number | 11 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-11-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data mining XML Motion pictures Relational databases Object oriented modeling Computer Society Image databases Object oriented databases Topology Web mining graph mining. Database applications data mining mining methods and algorithms |
| Content Type | Text |
| Resource Type | Article |
| Subject | Information Systems Computational Theory and Mathematics Computer Science Applications |
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