A distributed agent implementation of multiple species Flocking model for document partitioning clustering

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. © Springer-Verlag Berlin Heidelberg 2006.