Mining association rules with adjustable accuracy
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An Empirical Study of Domain Knowledge and Its Benefits to Substructure Discovery
IEEE Transactions on Knowledge and Data Engineering
Scalable Discovery of Informative Structural Concepts Using Domain Knowledge
IEEE Expert: Intelligent Systems and Their Applications
Representative Objects: Concise Representations of Semistructured, Hierarchial Data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
GraphDB: Modeling and Querying Graphs in Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Handbook of data mining and knowledge discovery
Constraint handling in multiobjective evolutionary optimization
IEEE Transactions on Evolutionary Computation
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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N-partite networks are natural representations of complex multi-entity databases. However, processing these networks can be a highly memory and computation-intensive task, especially when positive correlation exists between the degrees of vertices from different partitions. In order to improve the scalability of this process, this paper proposes two algorithms that make use of sampling for obtaining less expensive approximate results. The first algorithm is optimal for obtaining homogeneous discovery rates with a low memory requirement, but can be very slow in cases where the combined branching factor of these networks is too large. A second algorithm that incorporates concepts from evolutionary computation aims toward dealing with this slow convergence in the case when it is more interesting to increase approximation convergence speed of elements with high feature values. This algorithm makes use of the positive correlation between "local驴 branching factors and the feature values. Two applications examples are demonstrated in searching for most influential authors in collections of journal articles and in analyzing most active earthquake regions from a collection of earthquake events.