Ant Clustering Using Ensembles of Partitions

  • Yuhua Gu
  • Lawrence O. Hall
  • Dmitry B. Goldgof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)


Classical clustering algorithms require a predefined number of cluster centers. They are often very sensitive to initialization, which can result in very different clustering results. We present a two-phase algorithm which is a combination of a new ant based algorithm and a nonnegative matrix factorization-based consensus clustering algorithm. Ant clustering approaches can and do find the number of clusters as well as the data partition. However, they are very sensitive to both initial conditions and select parameters. Here, we show that using an ensemble of ant partitions and NMF to combine them we can find both the “right” number of clusters and a good data partition. Experiments were done with ten data sets. We conducted a wide range of comparisons that demonstrate the effectiveness of this new approach.


Clustering Ant-based clustering Fuzzy c-means Consensus clustering NMF Ensembles 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yuhua Gu
    • 1
  • Lawrence O. Hall
    • 1
  • Dmitry B. Goldgof
    • 1
  1. 1.Dept. of Computer Science & EngineeringUniversity of South FloridaTampaU.S.A.

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