2010 | OriginalPaper | Chapter
Decomposing Data Mining by a Process-Oriented Execution Plan
Authors : Yan Zhang, Honghui Li, Alexander Wöhrer, Peter Brezany, Gang Dai
Published in: Artificial Intelligence and Computational Intelligence
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Data mining deals with the extraction of hidden knowledge from large amounts of data. Nowadays, coarse-grained data mining modules are used. This traditional black box approach focuses on specific algorithm improvements and is not flexible enough to be used for more general optimization and beneficial component reuse. The work presented in this paper elaborates on decomposing data mining tasks as data mining execution process plans which are composed of finer-grained data mining operators. The cost of an operator can be analyzed and provides means for more holistic optimizations. This process-based data mining concept is evaluated via an OGSA-DAI based implementations for association rule mining which show the feasibility of our approach as well as the re-usability of some of the data mining operators.