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CINQ
2004
Springer
125views Database» more  CINQ 2004»
13 years 10 months ago
The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery
Many researchers in our community (this author included) regularly emphasize the role constraints play in improving performance of data-mining algorithms. This emphasis has led to ...
Roberto J. Bayardo
CINQ
2004
Springer
157views Database» more  CINQ 2004»
13 years 8 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
HIPC
2003
Springer
13 years 10 months ago
Parallel and Distributed Frequent Itemset Mining on Dynamic Datasets
Traditional methods for data mining typically make the assumption that data is centralized and static. This assumption is no longer tenable. Such methods waste computational and I/...
Adriano Veloso, Matthew Eric Otey, Srinivasan Part...
ML
2008
ACM
13 years 4 months ago
Discovering significant patterns
Pattern discovery techniques, such as association rule discovery, explore large search spaces of potential patterns to find those that satisfy some user-specified constraints. Due...
Geoffrey I. Webb
ECOI
2010
144views more  ECOI 2010»
13 years 3 months ago
Machine reasoning about anomalous sensor data
We describe a semantic data validation tool that is capable of observing incoming real-time sensor data and performing reasoning against a set of rules specific to the scientific d...
Matt Calder, Robert A. Morris, Francesco Peri