Abstract
Rather than finding new association-mining types one at a time, in this paper, we propose a framework, which is called Generalization of Association Mining via Information Granulation (GAMInG), based on which new association-mining types capable of discovering new patterns hidden in data can be systematically defined.
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References
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© 2004 Springer-Verlag Berlin Heidelberg
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Xie, Y., Raghavan, V.V. (2004). GAMInG – A Framework for Generalization of Association Mining via Information Granulation. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_23
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DOI: https://doi.org/10.1007/978-3-540-25929-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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