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» Modeling Relational Data as Graphs for Mining
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DSS
2007
127views more  DSS 2007»
15 years 4 months ago
Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining
We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified informationtheory-based Bayesian network algorithm and a modified asso...
Zan Huang, Jiexun Li, Hua Su, George S. Watts, Hsi...
IFIP12
2007
15 years 5 months ago
Clustering Improves the Exploration of Graph Mining Results
Mining frequent subgraphs is an area of research where we have a given set of graphs, and where we search for (connected) subgraphs contained in many of these graphs. Each graph ca...
Edgar H. de Graaf, Joost N. Kok, Walter A. Kosters
JIIS
2006
113views more  JIIS 2006»
15 years 4 months ago
Spatial ordering and encoding for geographic data mining and visualization
: Geographic information (e.g., locations, networks, and nearest neighbors) are unique and different from other aspatial attributes (e.g., population, sales, or income). It is a ch...
Diansheng Guo, Mark Gahegan
KDD
2012
ACM
164views Data Mining» more  KDD 2012»
13 years 6 months ago
SeqiBloc: mining multi-time spanning blockmodels in dynamic graphs
Blockmodelling is an important technique for decomposing graphs into sets of roles. Vertices playing the same role have similar patterns of interactions with vertices in other rol...
Jeffrey Chan, Wei Liu, Christopher Leckie, James B...
SDM
2010
SIAM
158views Data Mining» more  SDM 2010»
15 years 5 months ago
On the Use of Combining Rules in Relational Probability Trees
A relational probability tree (RPT) is a type of decision tree that can be used for probabilistic classification of instances with a relational structure. Each leaf of an RPT cont...
Daan Fierens