On efficient and effective association rule mining from XML data
Paper
Paper/Presentation Title | On efficient and effective association rule mining from XML data |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Ling, Tok Wang (Author), Bruckner, Robert (Author), Tjoa, A. Min (Author) and Liu, Han (Author) |
Editors | Galindo, Fernando, Takizawa, Makoto and Traunmuller, Roland |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 3180, pp. 497-507 |
Number of Pages | 11 |
Year | 2004 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783540229360 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-540-30075-5_48 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-540-30075-5_48 |
Conference/Event | 15th International Conference on Database and Expert Systems Applications (DEXA'04) |
Event Details | 15th International Conference on Database and Expert Systems Applications (DEXA'04) Event Date 30 Aug 2004 to end of 03 Sep 2004 Event Location Zaragoza, Spain |
Abstract | In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently and effectively. In XAR-Miner, raw XML data are first transformed to either an Indexed Content Tree (IX-tree) or Multirelational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection in the AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized meta-patterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent under-generalization or overgeneralization. Resultant generalized meta-patterns are used to generate large ARs that meet the support and confidence levels. An efficient AR mining algorithm is also presented based on candidate AR generation in the hierarchy of generalized meta-patterns. The experiments show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks. |
Keywords | association rule mining, XML data, meta-patterns |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | University of Toronto, Canada |
National University of Singapore | |
Microsoft, United States | |
Vienna University of Technology, Austria |
https://research.usq.edu.au/item/9z2qx/on-efficient-and-effective-association-rule-mining-from-xml-data
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