Mining Entropy l-Diversity Patterns

Authors:
Chaofeng Sha;Jian Gong;Aoying Zhou
Affiliations:
School of Computer Science, Fudan University, Shanghai, China 200433;School of Computer Science, Fudan University, Shanghai, China 200433;School of Computer Science, Fudan University, Shanghai, China 200433
Venue:
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Year:
2009

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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.