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SADM
2010
123views more  SADM 2010»
13 years 2 months ago
Discriminative frequent subgraph mining with optimality guarantees
The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discr...
Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei H...
SDM
2009
SIAM
149views Data Mining» more  SDM 2009»
14 years 1 months ago
Near-optimal Supervised Feature Selection among Frequent Subgraphs.
Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and an...
Alexander J. Smola, Arthur Gretton, Hans-Peter Kri...
ISSTA
2009
ACM
13 years 11 months ago
Identifying bug signatures using discriminative graph mining
Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Al...
Hong Cheng, David Lo, Yang Zhou, Xiaoyin Wang, Xif...
MLG
2007
Springer
13 years 10 months ago
Support Computation for Mining Frequent Subgraphs in a Single Graph
—Defining the support (or frequency) of a subgraph is trivial when a database of graphs is given: it is simply the number of graphs in the database that contain the subgraph. Ho...
Mathias Fiedler, Christian Borgelt
ICDM
2003
IEEE
154views Data Mining» more  ICDM 2003»
13 years 9 months ago
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
In this paper we study the problem of classifying chemical compound datasets. We present a sub-structure-based classification algorithm that decouples the sub-structure discovery...
Mukund Deshpande, Michihiro Kuramochi, George Kary...