TY - GEN
T1 - Mining entropy l-diversity patterns
AU - Sha, Chaofeng
AU - Gong, Jian
AU - Zhou, Aoying
PY - 2009
Y1 - 2009
N2 - 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 entropyl-diversity patterns, and propose some pruning strategies to speed our mining algorithm. Experiments show that our mining algorithm is fast in practice. For real date sets the running time are improved by serval orders of magnitude over brute force method.
AB - 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 entropyl-diversity patterns, and propose some pruning strategies to speed our mining algorithm. Experiments show that our mining algorithm is fast in practice. For real date sets the running time are improved by serval orders of magnitude over brute force method.
UR - https://www.scopus.com/pages/publications/67650146103
U2 - 10.1007/978-3-642-00887-0_34
DO - 10.1007/978-3-642-00887-0_34
M3 - 会议稿件
AN - SCOPUS:67650146103
SN - 9783642008863
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 384
EP - 388
BT - Database Systems for Advanced Applications - 14th International Conference, DASFAA 2009, Proceedings
T2 - 14th International Conference on Database Systems for Advanced Applications, DASFAA 2009
Y2 - 21 April 2009 through 23 April 2009
ER -