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An Incremental Learning Method for Data Mining from Large Databases


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Ling, TR 2006 , 'An Incremental Learning Method for Data Mining from Large Databases', Honours thesis, University of Tasmania.

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Knowledge Discovery techniques seek to find new information about a domain
through a combination of existing domain knowledge and data examples from the
domain. These techniques can either be manually performed by an expert, or
automated using software algorithms (Machine Learning). However some domains,
such as the clinical field of Lung Function testing, contain volumes of data too vast
and detailed for manual analysis to be effective, and existing knowledge too complex
for Machine Learning algorithms to be able to adequately discover relevant
knowledge. In many cases this data is also unclassified, with no previous analysis
having been performed. A better approach for these domains might be to involve a
human expert, taking advantage of their expertise to guide the process, and to use
Machine Learning techniques to assist the expert in discovering new and meaningful
relationships in the data. It is hypothesised that Knowledge Acquisition methods
would provide a strong basis for such a Knowledge Discovery method, particularly
methods which can provide incremental verification and validation of knowledge as
it is obtained. This study examines how the MCRDR (Multiple Classification Ripple-
Down Rules) Knowledge Acquisition process can be adapted to develop a new
Knowledge Discovery method, Exposed MCRDR, and tests this method in the
domain of Lung Function. Preliminary results suggest that the EMCRDR method can
be successfully applied to discover new knowledge in a complex domain, and reveal
many potential areas of study and development for the MCRDR method.

Item Type: Thesis - Honours
Authors/Creators:Ling, TR
Keywords: data mining, incremental learning, artificial intelligence, large databases
Publisher: Honours thesis, University of Tasmania
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