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» Efficient Discovery of Confounders in Large Data Sets
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KDD
2010
ACM
222views Data Mining» more  KDD 2010»
14 years 10 days ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...
ICDCS
2000
IEEE
14 years 2 months ago
Static and Adaptive Data Replication Algorithms for Fast Information Access in Large Distributed Systems
Creating replicas of frequently accessed objects across a read-intensive network can result in large bandwidth savings which, in turn, can lead to reduction in user response time....
Thanasis Loukopoulos, Ishfaq Ahmad
APWEB
2005
Springer
14 years 4 months ago
An Incremental Subspace Learning Algorithm to Categorize Large Scale Text Data
The dramatic growth in the number and size of on-line information sources has fueled increasing research interest in the incremental subspace learning problem. In this paper, we pr...
Jun Yan, QianSheng Cheng, Qiang Yang, Benyu Zhang
CINQ
2004
Springer
125views Database» more  CINQ 2004»
14 years 3 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
DMIN
2008
152views Data Mining» more  DMIN 2008»
13 years 12 months ago
PCS: An Efficient Clustering Method for High-Dimensional Data
Clustering algorithms play an important role in data analysis and information retrieval. How to obtain a clustering for a large set of highdimensional data suitable for database ap...
Wei Li 0011, Cindy Chen, Jie Wang