Abstract
Misleading caused by low quality data is a well known problem in knowledge discovery in databases. Several techniques have been introduced to deal with the problem which include inexact learning strategies, such as rough set based approaches and probabilistic approaches. This paper presents an approach for detecting trend using contribution functions. A trend directed method for the discovery of knowledge structure from low quality data bases is described. The experimental results show that trend directed methods are superior to other learning strategies, particularly when the learning is performed on low quality data bases.
This work was done at Monash university. At the time of preparing this final copy, the author has been employed by the School of Math and Compter Science, the University of New England at Armidale, NSW 2351, Australia.
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© 1998 Springer-Verlag Berlin Heidelberg
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Dai, H. (1998). Trend directed learning: A case study. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_6
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DOI: https://doi.org/10.1007/3-540-64383-4_6
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