Name
Abstract | ||
|---|---|---|
The more web data sources provide XML data, the greater information flood problem has been caused. Hence, there have been increasing demands for efficient methods of discovering desirable patterns from a large collection of XML data. In this paper, we propose a new and scalable algorithm, EFoX, to mine frequently occurring tree patterns from a set of labeled trees. The main contribution made by our algorithm is that there is no need to perform any tree join operation to generate candidate sets. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11428862_113 | International Conference on Computational Science (3) |
Keywords | Field | DocType |
main contribution,desirable pattern,scalable algorithm,frequent subtrees,xml data,efficient method,tree pattern,web data source,large collection,greater information flood problem,scalable method,candidate set | Data mining,XML,Computer science,Xml data,Knowledge extraction,Scalable algorithms,The Internet,Scalability,Search tree | Conference |
Volume | ISSN | ISBN |
3516 | 0302-9743 | 3-540-26044-7 |
Citations | PageRank | References |
6 | 0.49 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juryon Paik | 1 | 147 | 24.72 |
| Dong Ryeol Shin | 2 | 14 | 8.21 |
| Ungmo Kim | 3 | 58 | 11.90 |