An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Mining protein family specific residue packing patterns from protein structure graphs
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
A tutorial on support vector regression
Statistics and Computing
Mining Graph Data
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Correlation search in graph databases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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In this work we propose gRegress, a new algorithm which given set a of labeled graphs and a real value associated with each graph extracts the complete set of subgraphs such that a) each subgraph in this set has correlation with the real value above a user-specified threshold and b) each subgraph in this set has correlation with any other subgraph in the set below a user-specified threshold. gRegress incorporates novel pruning mechanisms based on correlation of a subgraph feature with the output and correlation with other subgraph features. These pruning mechanisms lead to significant speedup. Experimental results indicate that in terms of run-time, gRegress substantially outperforms gSpan, often by an order of magnitude while the regression models produced by both approaches have comparable accuracy.