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KDD
2000
ACM
101views Data Mining» more  KDD 2000»
13 years 9 months ago
Incremental quantile estimation for massive tracking
Data--call records, internet packet headers, or other transaction records--are coming down a pipe at a ferocious rate, and we need to monitor statistics of the data. There is no r...
Fei Chen, Diane Lambert, José C. Pinheiro
SDM
2010
SIAM
195views Data Mining» more  SDM 2010»
13 years 7 months ago
MACH: Fast Randomized Tensor Decompositions
Tensors naturally model many real world processes which generate multi-aspect data. Such processes appear in many different research disciplines, e.g, chemometrics, computer visio...
Charalampos E. Tsourakakis
KDD
2008
ACM
165views Data Mining» more  KDD 2008»
14 years 6 months ago
Colibri: fast mining of large static and dynamic graphs
Low-rank approximations of the adjacency matrix of a graph are essential in finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track ...
Hanghang Tong, Spiros Papadimitriou, Jimeng Sun, P...
KDD
2000
ACM
149views Data Mining» more  KDD 2000»
13 years 9 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
CIKM
2008
Springer
13 years 8 months ago
On effective presentation of graph patterns: a structural representative approach
In the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. However, the...
Chen Chen, Cindy Xide Lin, Xifeng Yan, Jiawei Han