Abstract
This paper presents an extended granule mining based methodology, to effectively describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. We also provide an algorithm to implement the proposed methodology. The experiments conducted to characterize a real network traffic data collection show that the proposed concepts and algorithm are promising.
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Liu, B., Li, Y., Tian, YC. (2012). Discovering Novel Knowledge Using Granule Mining. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_45
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DOI: https://doi.org/10.1007/978-3-642-32115-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32114-6
Online ISBN: 978-3-642-32115-3
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