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PPOPP
2005
ACM
14 years 1 months ago
A sampling-based framework for parallel data mining
The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data minin...
Shengnan Cong, Jiawei Han, Jay Hoeflinger, David A...
ICDE
2005
IEEE
118views Database» more  ICDE 2005»
14 years 9 months ago
Scrutinizing Frequent Pattern Discovery Performance
Benchmarking technical solutions is as important as the solutions themselves. Yet many fields still lack any type of rigorous evaluation. Performance benchmarking has always been ...
Mohammad El-Hajj, Osmar R. Zaïane, Stella Luk...
KDD
2010
ACM
214views Data Mining» more  KDD 2010»
13 years 11 months ago
Neighbor query friendly compression of social networks
Compressing social networks can substantially facilitate mining and advanced analysis of large social networks. Preferably, social networks should be compressed in a way that they...
Hossein Maserrat, Jian Pei
CIKM
2008
Springer
13 years 9 months ago
Structure feature selection for graph classification
With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining...
Hongliang Fei, Jun Huan
KDD
1997
ACM
92views Data Mining» more  KDD 1997»
13 years 11 months ago
Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation
This paper describes how to increase the efficiency of inductive data mining algorithms by replacing the central matching operation with a marker propagation technique. Breadth-fi...
John M. Aronis, Foster J. Provost