Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
A flocking based algorithm for document clustering analysis
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
KNIME - the Konstanz information miner: version 2.0 and beyond
ACM SIGKDD Explorations Newsletter
Advances in Knowledge Discovery and Management
Advances in Knowledge Discovery and Management
Neighborhood density method for selecting initial cluster centers in k-means clustering
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A neighborhood-based clustering algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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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.