System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
IEEE Intelligent Systems
Enhancing Structure Discovery for Data Mining in Graphical Databases Using Evolutionary Programming
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Visualizing the Structure of Science
Visualizing the Structure of Science
DENGRAPH: A Density-based Community Detection Algorithm
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
SkyGraph: an algorithm for important subgraph discovery in relational graphs
Data Mining and Knowledge Discovery
A quick MST-based algorithm to obtain Pathfinder networks (∞, n - 1)
Journal of the American Society for Information Science and Technology
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Mining (Social) Network Graphs to Detect Random Link Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Information Sciences: an International Journal
Hierarchical Pattern Discovery in Graphs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A multiobjective evolutionary programming framework for graph-based data mining
Information Sciences: an International Journal
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The existing methods for graph-based data mining (GBDM) follow the basic approach of applying a single-objective search with a user-defined threshold to discover interesting subgraphs. This obliges the user to deal with simple thresholds and impedes her/him from evaluating the mined subgraphs by defining different ''goodness'' (i.e., multiobjective) criteria regarding the characteristics of the subgraphs. In previous papers, we defined a multiobjective GBDM framework to perform bi-objective graph mining in terms of subgraph support and size maximization. Two different search methods were considered with this aim, a multiobjective beam search and a multiobjective evolutionary programming (MOEP). In this contribution, we extend the latter formulation to a three-objective framework by incorporating another classical graph mining objective, the subgraph diameter. The proposed MOEP method for multiobjective GBDM is tested on five synthetic and real-world datasets and its performance is compared against single and multiobjective subgraph mining approaches based on the classical Subdue technique in GBDM. The results highlight the application of multiobjective subgraph mining allows us to discover more diversified subgraphs in the objective space.