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» Approximate data mining in very large relational data
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PAKDD
2004
ACM
199views Data Mining» more  PAKDD 2004»
15 years 9 months ago
Temporal Sequence Associations for Rare Events
In many real world applications, systematic analysis of rare events, such as credit card frauds and adverse drug reactions, is very important. Their low occurrence rate in large da...
Jie Chen, Hongxing He, Graham J. Williams, Huidong...
SDM
2010
SIAM
182views Data Mining» more  SDM 2010»
15 years 5 months ago
HCDF: A Hybrid Community Discovery Framework
We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework), can effectively incorporate...
Keith Henderson, Tina Eliassi-Rad, Spiros Papadimi...
ISCA
1993
IEEE
112views Hardware» more  ISCA 1993»
15 years 8 months ago
Working Sets, Cache Sizes, and Node Granularity Issues for Large-Scale Multiprocessors
The distribution of resources among processors, memory and caches is a crucial question faced by designers of large-scale parallel machines. If a machine is to solve problems with...
Edward Rothberg, Jaswinder Pal Singh, Anoop Gupta
BMCBI
2011
14 years 11 months ago
PheMaDB: A solution for storage, retrieval, and analysis of high throughput phenotype data
Background: OmniLog™ phenotype microarrays (PMs) have the capability to measure and compare the growth responses of biological samples upon exposure to hundreds of growth condit...
Wenling E. Chang, Keri Sarver, Brandon W. Higgs, T...
SDM
2008
SIAM
139views Data Mining» more  SDM 2008»
15 years 5 months ago
Maximal Quasi-Bicliques with Balanced Noise Tolerance: Concepts and Co-clustering Applications
The rigid all-versus-all adjacency required by a maximal biclique for its two vertex sets is extremely vulnerable to missing data. In the past, several types of quasi-bicliques ha...
Jinyan Li, Kelvin Sim, Guimei Liu, Limsoon Wong