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» Mining Multiple Large Databases
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ICPADS
2006
IEEE
14 years 1 months ago
Parallel Leap: Large-Scale Maximal Pattern Mining in a Distributed Environment
When computationally feasible, mining extremely large databases produces tremendously large numbers of frequent patterns. In many cases, it is impractical to mine those datasets d...
Mohammad El-Hajj, Osmar R. Zaïane
CINQ
2004
Springer
157views Database» more  CINQ 2004»
13 years 11 months ago
Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach
Inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operat...
Jean-François Boulicaut
VLDB
1998
ACM
180views Database» more  VLDB 1998»
13 years 12 months ago
Active Storage for Large-Scale Data Mining and Multimedia
The increasing performance and decreasing cost of processors and memory are causing system intelligence to move into peripherals from the CPU. Storage system designers are using t...
Erik Riedel, Garth A. Gibson, Christos Faloutsos
SIGMOD
2008
ACM
131views Database» more  SIGMOD 2008»
14 years 7 months ago
Discovering topical structures of databases
The increasing complexity of enterprise databases and the prevalent lack of documentation incur significant cost in both understanding and integrating the databases. Existing solu...
Wensheng Wu, Berthold Reinwald, Yannis Sismanis, R...
ESWA
2008
101views more  ESWA 2008»
13 years 7 months ago
Discovering during-temporal patterns (DTPs) in large temporal databases
Abstract Large temporal Databases (TDBs) usually contain a wealth of data about temporal events. Aimed at discovering temporal patterns with during relationship (during-temporal pa...
Li Zhang, Guoqing Chen, Tom Brijs, Xing Zhang