Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
ACM Computing Surveys (CSUR)
Information Flocking: Time-Varying Data Visualization using Boid Behaviors
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Ant-Based Clustering and Topographic Mapping
Artificial Life
A flocking based algorithm for document clustering analysis
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Analysis of mammography reports using maximum variation sampling
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A single pass algorithm for clustering evolving data streams based on swarm intelligence
Data Mining and Knowledge Discovery
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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.