EverMiner: consideration on knowledge driven permanent data mining process

Jan Rauch

International Journal of Data Mining, Modelling and Management, 2012, vol. 4, issue 3, 224-243

Abstract: A data mining task is usually solved in six main steps described in the CRISP-DM methodology. The paper introduces another approach to data mining. Data mining is understood as a permanent knowledge driven process. It is assumed that there is a well organised knowledge repository containing both relevant domain knowledge and new knowledge found in the analysed data. It is also assumed that there are several tools that formulate reasonable data mining tasks, search in the analysed data for true patterns relevant to the formulated tasks, filter out found patterns that can be understood as the consequences of items of knowledge stored in the repository, synthesise new items of knowledge from the remaining patterns and store items of new knowledge in the knowledge repository. It is argued that a system consisting of such tools can be built from already existing tools based on the GUHA method and observational calculi.

Keywords: data mining; CRISP-DM; knowledge driven process; GUHA method; association rules; observational calculi; deduction rules; LISp-Miner system; 4ft-Miner. (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=48105 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:4:y:2012:i:3:p:224-243

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().