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Deep Web Sources Classifier Based on DSOM-EACO Clustering Model

  • Yong Feng
  • Xianyong Chen
  • Zhen Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)

Abstract

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.

Keywords

deep web sources classification classifier dynamic self-organizing maps ant colony optimization clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yong Feng
    • 1
  • Xianyong Chen
    • 1
  • Zhen Chen
    • 1
  1. 1.College of Computer ScienceChongqing UniversityChongqingChina

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