Knowledge discovery in databases using lattices

The rapid pace at which data gathering, storage and distribution technologies are developing is outpacing our advances in techniques for helping humans to analyse, understand, and digest the vast amounts of resulting data. This has led to the birth of knowledge discovery in databases (KDD) and data...

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Bibliographic Details
Published in:Expert systems with applications 1997-11, Vol.13 (4), p.259-264
Main Authors: Venter, F.J., Oosthuizen, G.D., Roos, J.D.
Format: Article
Language:English
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Summary:The rapid pace at which data gathering, storage and distribution technologies are developing is outpacing our advances in techniques for helping humans to analyse, understand, and digest the vast amounts of resulting data. This has led to the birth of knowledge discovery in databases (KDD) and data mining—a process that has the goal to selectively extract knowledge from data. A range of techniques, including neural networks, rule-based systems, case-based reasoning, machine learning, statistics, etc. can be applied to the problem. We discuss the use of concept lattices, to determine dependences in the data mining process. We first define concept lattices, after which we show how they represent knowledge and how they are formed from raw data. Finally, we show how the lattice-based technique addresses different processes in KDD, especially visualization and navigation of discovered knowledge.
ISSN:0957-4174
1873-6793
DOI:10.1016/S0957-4174(97)00047-X