Abstract
The Flocking model, first proposed by Craig Reynolds, is one of the first bio-inspired computational collective behavior models that has many popular applications, such as animation. Our early research has resulted in a flock clustering algorithm that can achieve better performance than the K-means or the Ant clustering algorithms for data clustering. This algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for efficient clustering result retrieval and visualization. In this paper, we propose a bio-inspired clustering model, the Multiple Species Flocking clustering model (MSF), and present a distributed multi-agent MSF approach for document clustering.
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Cui, X., Potok, T.E. (2006). A Distributed Agent Implementation of Multiple Species Flocking Model for Document Partitioning Clustering. In: Klusch, M., Rovatsos, M., Payne, T.R. (eds) Cooperative Information Agents X. CIA 2006. Lecture Notes in Computer Science(), vol 4149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839354_10
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DOI: https://doi.org/10.1007/11839354_10
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