文章基本信息

标题：Mining Frequent Closed Ordered Subtrees under Anti-monotone Constraints by using Restricted Occurrence Matching
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作者：Tomonobu Ozaki ; Takenao Ohkawa
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2008
卷号：23
期号：2
页码：58-67
DOI：10.1527/tjsai.23.58
出版社：The Japanese Society for Artificial Intelligence
摘要：As semi-structured data is used widely in several fields, the importance of structured data mining is increasing recently. Although mining frequent patterns in structured data is one of the most fundamental tasks, frequent pattern miners often discover huge number of patterns. To overcome this problem, two major approaches, condensed representation mining and constraint-based mining, have been proposed. In this paper, as a technique for integrating these two approaches, we propose three algorithms, RC_LOCOT, posC_LOCOT, and negC_LOCOT, for discovering closed ordered subtrees under anti-monotone constraints about the structure of patterns to be discovered. The proposed algorithms discover closed constrained subtrees efficiently not by post-processing but by pruning and skipping the search space based on the occurrence matching and the patterns on the border.
关键词：semi-structured data mining ; ordered subtrees ; closed patterns ; constrained patterns ; pruning