On data clustering with a flock of artificial agents

We present a new bio-inspired algorithm that dynamically creates and visualizes groups of data. This algorithm uses the concepts of a flock of agents that move together in a complex manner with simple local rules. Each agent represents one data. The agents move with the aim of creating homogeneous groups of data that evolve together in a 2D environment. These created groups are visualized in real time and help the domain expert to understand the underlying class structure of the data set, like for example a realistic number of classes, clusters of similar data, isolated data, etc. We present several extensions of this algorithm and present results from artificial and real-world data.