Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
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
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
On Data Clustering with a Flock of Artificial Agents
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Time-Varying Data Visualization Using Information Flocking Boids
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
A flocking based algorithm for document clustering analysis
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
A New Approach of Data Clustering Using a Flock of Agents
Evolutionary Computation
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
Clustering and dynamic data visualization with artificial flying insect
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We explore the notion of agent-based data mining and visualization as a means for exploring large, multi-dimensional data sets. In Reynolds' classic flocking algorithm (1987), individuals move in a 2-dimensional space and emulate the behavior of a flock of birds (or "boids", as Reynolds refers to them). Each individual in the simulated flock exhibits specific behaviors that dictate how it moves and how it interacts with other boids in its "neighborhood". We are interested in using this approach as a way of visualizing large multi-dimensional data sets. In particular, we are focused on data sets in which records contain time-tagged information about people (e.g., a student in an educational data set or a patient in a medical records data set). We present a system in which individuals in the data set are represented as agents, or "data boids". The flocking exhibited by our boids is driven not by observation and emulation of creatures in nature, but rather by features inherent in the data set. The visualization quickly shows separation of data boids into clusters, where members are attracted to each other by common feature values.