MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation
Tree mining has many useful applications in areas such as Bioinformatics, XML mining, Web mining, etc. In general, most of the formally represented information in these domains is a tree structured form. In this paper we focus on mining frequent embedded subtrees from databases of rooted labelled or...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
IEEE
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/33568 |
| _version_ | 1848753982848106496 |
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| author | Chang, Elizabeth Tan, H. Dillon, Tharam S. Hadzic, Fedja Feng, L. |
| author_facet | Chang, Elizabeth Tan, H. Dillon, Tharam S. Hadzic, Fedja Feng, L. |
| author_sort | Chang, Elizabeth |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Tree mining has many useful applications in areas such as Bioinformatics, XML mining, Web mining, etc. In general, most of the formally represented information in these domains is a tree structured form. In this paper we focus on mining frequent embedded subtrees from databases of rooted labelled ordered subtrees. We propose a novel and unique embedding list representation that is suitable for describing embedded subtrees. This representation is completely different from the string-like or conventional adjacency list representation previously utilized for trees. We present the mathematical model of a breadth-first-search Tree Model Guided (TMG) candidate generation approach previously introduced in [8]. The key characteristic of the TMG approach is that it enumerates fewer candidates by ensuring that only valid candidates that conform to the structural aspects of the data are generated as opposed to the join approach. Our experiments with both synthetic and real-life datasets provide comparisons against one of the state-of-the-art algorithms, TreeMiner [15], and they demonstrate the effectiveness and the efficiency of the technique. |
| first_indexed | 2025-11-14T08:33:10Z |
| format | Conference Paper |
| id | curtin-20.500.11937-33568 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:33:10Z |
| publishDate | 2005 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-335682017-01-30T13:37:56Z MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation Chang, Elizabeth Tan, H. Dillon, Tharam S. Hadzic, Fedja Feng, L. embedded subtree tree model guided information systems TMG frequent tree mining treeminer tree mining Tree mining has many useful applications in areas such as Bioinformatics, XML mining, Web mining, etc. In general, most of the formally represented information in these domains is a tree structured form. In this paper we focus on mining frequent embedded subtrees from databases of rooted labelled ordered subtrees. We propose a novel and unique embedding list representation that is suitable for describing embedded subtrees. This representation is completely different from the string-like or conventional adjacency list representation previously utilized for trees. We present the mathematical model of a breadth-first-search Tree Model Guided (TMG) candidate generation approach previously introduced in [8]. The key characteristic of the TMG approach is that it enumerates fewer candidates by ensuring that only valid candidates that conform to the structural aspects of the data are generated as opposed to the join approach. Our experiments with both synthetic and real-life datasets provide comparisons against one of the state-of-the-art algorithms, TreeMiner [15], and they demonstrate the effectiveness and the efficiency of the technique. 2005 Conference Paper http://hdl.handle.net/20.500.11937/33568 IEEE fulltext |
| spellingShingle | embedded subtree tree model guided information systems TMG frequent tree mining treeminer tree mining Chang, Elizabeth Tan, H. Dillon, Tharam S. Hadzic, Fedja Feng, L. MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation |
| title | MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation |
| title_full | MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation |
| title_fullStr | MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation |
| title_full_unstemmed | MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation |
| title_short | MB3-Miner: Efficient mining eMBedded subTREEs using tree model guided candidate generation |
| title_sort | mb3-miner: efficient mining embedded subtrees using tree model guided candidate generation |
| topic | embedded subtree tree model guided information systems TMG frequent tree mining treeminer tree mining |
| url | http://hdl.handle.net/20.500.11937/33568 |