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» Discovering Itemset Interactions
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PPOPP
2005
ACM
15 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...
PAKDD
2005
ACM
124views Data Mining» more  PAKDD 2005»
15 years 3 months ago
Finding Sporadic Rules Using Apriori-Inverse
We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the w...
Yun Sing Koh, Nathan Rountree
JIIS
2006
119views more  JIIS 2006»
14 years 9 months ago
Answering constraint-based mining queries on itemsets using previous materialized results
Abstract In recent years, researchers have begun to study inductive databases, a new generation of databases for leveraging decision support applications. In this context, the user...
Roberto Esposito, Rosa Meo, Marco Botta
BMCBI
2005
150views more  BMCBI 2005»
14 years 9 months ago
Discover protein sequence signatures from protein-protein interaction data
Background: The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-prot...
Jianwen Fang, Ryan J. Haasl, Yinghua Dong, Gerald ...
BMCBI
2005
126views more  BMCBI 2005»
14 years 9 months ago
HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms
Background: The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some ...
Maria Persico, Arnaud Ceol, Caius Gavrila, Robert ...