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» Large Scale Data Mining: Challenges and Responses
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SIGMOD
1998
ACM
99views Database» more  SIGMOD 1998»
15 years 10 months ago
CURE: An Efficient Clustering Algorithm for Large Databases
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clust...
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
DOLAP
2010
ACM
15 years 4 months ago
Relational versus non-relational database systems for data warehousing
Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design, has a close r...
Carlos Ordonez, Il-Yeol Song, Carlos Garcia-Alvara...
ISCA
2006
IEEE
148views Hardware» more  ISCA 2006»
16 years 4 days ago
Tolerating Dependences Between Large Speculative Threads Via Sub-Threads
Thread-level speculation (TLS) has proven to be a promising method of extracting parallelism from both integer and scientific workloads, targeting speculative threads that range ...
Christopher B. Colohan, Anastassia Ailamaki, J. Gr...
BMCBI
2005
97views more  BMCBI 2005»
15 years 6 months ago
The yeast kinome displays scale free topology with functional hub clusters
Background: The availability of interaction databases provides an opportunity for researchers to utilize immense amounts of data exclusively in silico. Recently there has been an ...
Robin E. C. Lee, Lynn A. Megeney
SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
15 years 7 months ago
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
Zheng Zhao, Huan Liu