Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Self-Organizing Maps
Computer
Automatic complex schema matching across Web query interfaces: A correlation mining approach
ACM Transactions on Database Systems (TODS)
Communications of the ACM - ACM at sixty: a look back in time
An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Detection Classifier
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Domain-Specific Deep Web Sources Discovery
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
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There are many deep web sources providing the services, but we may not be aware of their existence, and not know which sources can satisfy our demands. So that there is a great significant to build a system to integrate the myriad deep web sources in the Internet, and the classification of deep web sources is very important in the integration. In this paper, a clustering model based on dynamic self-organizing maps (DSOM) and enhanced ant colony optimization (EACO) is systematically proposed for deep web sources classification. The basic idea of the model is to produce the cluster by DSOM and EACO. With the classified data instances, the classifier can be established. And then the classifier can be used in real deep web sources classification, and it is observed that the proposed approach gives better performance over some traditional approaches for deep web sources classification problems.