Abstract
This paper investigates the constrained clustering problem through swarm intelligence. We present an ant clustering algorithm based on random walk to deal with the pairwise constrained clustering problems. Our algorithm mimics the behaviors of the real-world ant colonies and produces better clustering result on both synthetic and UCI datasets compared with the unsupervised ant-based clustering algorithm and the cop-kmeans algorithm.
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Xu, X., Pan, Z., He, P., Chen, L. (2012). Constrained Clustering via Swarm Intelligence. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_53
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DOI: https://doi.org/10.1007/978-3-642-24553-4_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24552-7
Online ISBN: 978-3-642-24553-4
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