.png){kind=icon}
.png){kind=icon}
Return
Metaqueries: Semantics, complexity, and efficient algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/S0004-3702(03)00073-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Metaquery (metapattern) is a data mining tool which is useful for learning rules involving more than one relation in the database. The notion of a metaquery has been proposed as a template or a second-order proposition in a language C that describes the type of pattern to be discovered. This tool has already been successfully applied to several real-world applications. In this paper we advance the state of the art in metaquery research in several ways. First, we argue that the notion of a support value for metaqueries, where a support value is intuitively some indication to the relevance of the rules to be discovered, is not adequately defined in the literature, and, hence, propose our own definition. Second, we analyze some of the related computational problems, classify them as NP-hard and point out some tractable cases. Third, we propose some efficient algorithms for computing support and present preliminary experimental results that indicate the usefulness of our algorithms. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
knowledge discovery
data mining
metaqueries
support
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.9
Papers:
6.1K
Citations:
1.9W
Organization
No organization information available