Ant Clustering Using Ensembles of Partitions

Authors:
Yuhua Gu;Lawrence O. Hall;Dmitry B. Goldgof
Affiliations:
Dept. of Computer Science & Engineering, University of South Florida, Tampa, U.S.A. FL 33620;Dept. of Computer Science & Engineering, University of South Florida, Tampa, U.S.A. FL 33620;Dept. of Computer Science & Engineering, University of South Florida, Tampa, U.S.A. FL 33620
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

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Abstract

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.