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» Mining Very Large Databases
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ICDM
2002
IEEE
156views Data Mining» more  ICDM 2002»
14 years 3 months ago
Association Analysis with One Scan of Databases
Mining frequent patterns with an FP-tree avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves bette...
Hao Huang, Xindong Wu, Richard Relue
KDD
1995
ACM
135views Data Mining» more  KDD 1995»
14 years 1 months ago
Rough Sets Similarity-Based Learning from Databases
Manydata mining algorithms developed recently are based on inductive learning methods. Very few are based on similarity-based learning. However, similarity-based learning accrues ...
Xiaohua Hu, Nick Cercone
BMCBI
2007
102views more  BMCBI 2007»
13 years 10 months ago
Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms
Background: The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usua...
Luis A. Diago, Persy Morell, Longendri Aguilera, E...
ICSM
1998
IEEE
14 years 2 months ago
Identification of Data Cohesive Subsystems Using Data Mining Techniques
The activity of reengineering and maintaining large legacy systems involves the use of design recovery es to produce abstractions that facilitate the understanding of the system. ...
Carlos Montes de Oca, Doris L. Carver
KDD
2006
ACM
208views Data Mining» more  KDD 2006»
14 years 10 months ago
Frequent subgraph mining in outerplanar graphs
In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining pro...
Tamás Horváth, Jan Ramon, Stefan Wro...