Abstract
In the April 2010 Nature research report, it was announced that biological physicists only very recently discovered that there exists a leadership pattern in flocks of pigeon birds. The most authoritative birds of the pigeons’ flock take the lead, and followers follow the leaders’ directions. Pigeon leaders’ roles vary over time. Following this unprecedented discovery made by zoologists at the University of Oxford and Eötvös University, we extend in this paper the flocking model largely used in computer science. We define a new biologically inspired clustering algorithm entitled “FlockbyLeade” that detects hierarchical leaders, discovers their followers, and enables them to flock based on local proximity in an artificial virtual space to create clusters. We offer empirical evidence that the algorithm outperforms both the existing flocking algorithm and the K-means algorithm. We analyze the performance of the algorithm based on widely used datasets in the literature.
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Bellaachia, A., Bari, A. (2012). Flock by Leader: A Novel Machine Learning Biologically Inspired Clustering Algorithm. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_15
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DOI: https://doi.org/10.1007/978-3-642-31020-1_15
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