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» Mining evolving data streams for frequent patterns
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ADMA
2008
Springer
147views Data Mining» more  ADMA 2008»
15 years 4 months ago
Mining Supplemental Frequent Patterns
The process of resource distribution and load balance of a distributed P2P network can be described as the process of mining Supplement Frequent Patterns (SFPs) from query transact...
Yintian Liu, Yingming Liu, Tao Zeng, Kaikuo Xu, Ro...
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
15 years 10 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
ADMA
2005
Springer
124views Data Mining» more  ADMA 2005»
14 years 11 months ago
Finding All Frequent Patterns Starting from the Closure
Efficient discovery of frequent patterns from large databases is an active research area in data mining with broad applications in industry and deep implications in many areas of d...
Mohammad El-Hajj, Osmar R. Zaïane
ICDM
2002
IEEE
145views Data Mining» more  ICDM 2002»
15 years 2 months ago
Mining Top-K Frequent Closed Patterns without Minimum Support
In this paper, we propose a new mining task: mining top-k frequent closed patterns of length no less than min , where k is the desired number of frequent closed patterns to be min...
Jiawei Han, Jianyong Wang, Ying Lu, Petre Tzvetkov
CIS
2005
Springer
15 years 3 months ago
An Improved EMASK Algorithm for Privacy-Preserving Frequent Pattern Mining
Abstract. As a novel research direction, privacy-preserving data mining (PPDM) has received a great deal of attentions from more and more researchers, and a large number of PPDM al...
Congfu Xu, Jinlong Wang, Hongwei Dan, Yunhe Pan