Abstract
The discovery of diversity patterns from binary data is an important data mining task. This paper proposes entropy l-diversity patterns based on information theory, and develops techniques for discovering such diversity patterns. We study the properties of the entropy l-diversity patterns, and propose some pruning strategies to speed our mining algorithm. Experiments show that our mining algorithm is fast in practice. For real datesets the running time are improved by serval orders of magnitude over brute force method.
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Sha, C., Gong, J., Zhou, A. (2009). Mining Entropy l-Diversity Patterns. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00887-0_34
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DOI: https://doi.org/10.1007/978-3-642-00887-0_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00886-3
Online ISBN: 978-3-642-00887-0
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