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DATAMINE
2007
101views more  DATAMINE 2007»
14 years 11 months ago
Using metarules to organize and group discovered association rules
The high dimensionality of massive data results in the discovery of a large number of association rules. The huge number of rules makes it difficult to interpret and react to all ...
Abdelaziz Berrado, George C. Runger
PAKDD
2011
ACM
209views Data Mining» more  PAKDD 2011»
14 years 2 months ago
Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation
Abstract. Given a graph with billions of nodes and edges, how can we find patterns and anomalies? Are there nodes that participate in too many or too few triangles? Are there clos...
U. Kang, Brendan Meeder, Christos Faloutsos
TKDE
2012
190views Formal Methods» more  TKDE 2012»
13 years 2 months ago
Scalable Learning of Collective Behavior
—This study of collective behavior is to understand how individuals behave in a social networking environment. Oceans of data generated by social media like Facebook, Twitter, Fl...
Lei Tang, Xufei Wang, Huan Liu
CORR
2011
Springer
151views Education» more  CORR 2011»
14 years 3 months ago
A Stochastic-Geometry Approach to Coverage in Cellular Networks with Multi-Cell Cooperation
—Multi-cell cooperation is a promising approach for mitigating inter-cell interference in dense cellular networks. Quantifying the performance of multi-cell cooperation is challe...
Kaibin Huang, Jeffrey G. Andrews
SDM
2009
SIAM
192views Data Mining» more  SDM 2009»
15 years 9 months ago
Mining Cohesive Patterns from Graphs with Feature Vectors.
The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to g...
Arash Rafiey, Flavia Moser, Martin Ester, Recep Co...