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A New Space Defined by Ant Colony Algorithm to Partition Data

  • Hamid Parvin
  • Behrouz Minaei-Bidgoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6943)

Abstract

To reach a robust partition, ensemble-based learning is always a very promising option. There is straightforward way to generate a set of primary partitions that are different from each other, and then to aggregate the partitions via a consensus function to generate the final partition. Another alternative in the ensemble learning is to turn to fusion of different data from originally different sources. In this paper we introduce a new ensemble learning based on the Ant Colony clustering algorithm. Experimental results on some real-world datasets are presented to demonstrate the effectiveness of the proposed method in generating the final partition.

Keywords

Ant Colony Data Fusion Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hamid Parvin
    • 1
  • Behrouz Minaei-Bidgoli
    • 1
  1. 1.School of Computer EngineeringIran University of Science and Technology (IUST)TehranIran

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