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SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
16 years 3 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
169
Voted
PODS
2007
ACM
225views Database» more  PODS 2007»
16 years 3 months ago
Sketching unaggregated data streams for subpopulation-size queries
IP packet streams consist of multiple interleaving IP flows. Statistical summaries of these streams, collected for different measurement periods, are used for characterization of ...
Edith Cohen, Nick G. Duffield, Haim Kaplan, Carste...
EDBT
2009
ACM
132views Database» more  EDBT 2009»
15 years 10 months ago
A novel approach for efficient supergraph query processing on graph databases
In recent years, large amount of data modeled by graphs, namely graph data, have been collected in various domains. Efficiently processing queries on graph databases has attracted...
Shuo Zhang, Jianzhong Li, Hong Gao, Zhaonian Zou
GLOBECOM
2009
IEEE
15 years 10 months ago
Sparse Decoding of Low Density Parity Check Codes Using Margin Propagation
—One of the key factors underlying the popularity of Low-density parity-check (LDPC) code is its iterative decoding algorithm that is amenable to efficient hardware implementati...
Ming Gu, Kiran Misra, Hayder Radha, Shantanu Chakr...
ICDM
2009
IEEE
160views Data Mining» more  ICDM 2009»
15 years 10 months ago
Fast Online Training of Ramp Loss Support Vector Machines
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t...
Zhuang Wang, Slobodan Vucetic
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