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Collaborative Knowledge Discovery & Data Mining: From Knowledge to Experience

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2 Author(s)
Timo Horeis ; Faculty of Computer Science and Mathematics, University of Passau, Germany. e-mail: ; Bernhard Sick

Experts have important qualitative knowledge about interrelations between more or less abstract concepts in an application area. However, the knowledge of a single expert is typically quite uncertain (e.g., incomplete or imprecise). By fusing the knowledge of several experts it would be possible to obtain more certain and, therefore, more valuable knowledge. Conventional systems for knowledge discovery (KD) and data mining (DM) have the ability to extract valid rules from huge data sets. These rules describe dependencies between attributes and classes in a quantitative way, for instance. By fusing this kind of knowledge with the combined, qualitative knowledge of several experts it would be possible to obtain more comprehensive knowledge about an application area. In this article, we propose a concept for a new KD & DM technique based on computational intelligence: collaborative knowledge discovery (CKD). These techniques combines the uncertain knowledge of several experts using methods based on Dempster-Shafer theory. The combined human knowledge is again fused with automatically extracted, well interpretable knowledge (fuzzy rules embedded in a radial basis function neural network) of a conventional KD system. Thus, a CKD system not only acquires more comprehensive knowledge, but also experience (knowledge about knowledge), meaning that it is able to explain automatically extracted rules to the human experts and to assess the interestingness (e.g., novelty or utility) of these rules. This can be done by adapting inference mechanisms from the field of probabilistic argumentation systems. A CKD system will comprise self-awareness mechanisms (it must know what it knows) as well as environment-awareness mechanisms (it must know what human experts know or what they want to now). In order to reduce the effort for knowledge acquisition, a CKD system must learn (pro-)actively. There are many application areas for such CKD systems, e.g., in the field of technical data- mining (quality control, process monitoring, etc.)

Published in:

Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Date of Conference:

March 1 2007-April 5 2007