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» Mining Very Large Databases
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PPOPP
2005
ACM
14 years 3 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...
DEXAW
1998
IEEE
116views Database» more  DEXAW 1998»
14 years 2 months ago
Data-Mining: A Tightly-Coupled Implementation on a Parallel Database Server
Due to the increasingly di culty of discovering patterns in real-world databases using only conventional OLAP tools, an automated process such as data mining is currently essentia...
Mauro Sousa, Marta Mattoso, Nelson F. F. Ebecken
NLDB
2005
Springer
14 years 3 months ago
Combining Biological Databases and Text Mining to Support New Bioinformatics Applications
Abstract. A large amount of biological knowledge today is only available from full-text research papers. Since neither manual database curators nor users can keep up with the rapid...
René Witte, Christopher J. O. Baker
ICTAI
2003
IEEE
14 years 3 months ago
Parallel Mining of Maximal Frequent Itemsets from Databases
In this paper, we propose a parallel algorithm for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. The new par...
Soon Myoung Chung, Congnan Luo
BMCBI
2005
108views more  BMCBI 2005»
13 years 10 months ago
Storing, linking, and mining microarray databases using SRS
Background: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range ...
Antoine Veldhoven, Don de Lange, Marcel Smid, Vict...