Data Swarm Clustering

Data clustering is concerned with the division of a set of objects into groups of similar objects. In social insects there are many examples of clustering processes. Brood sorting observed in ant colonies can be considered as clustering according to the developmental state of the larvae. Also nest cleaning by forming piles of corpse or items is another example. These observed sorting and cluster capabilities of ant colonies have already been the inspiration of an ant-based clustering algorithm. Another kind of clustering mechanism can be observed in flocks of birds. In some rainforests mixed-species flocks of birds can be observed. From time to time different species of birds are merging to become a multi-species swarm. The separation of this multi-species swarm into its single species can be considered as a kind of species clustering. This chapter introduces a data clustering algorithm based on species clustering. It combines methods of Particle Swarm Optimization and Flock Algorithms. A given set of data is interpreted as a multi-species swarm which wants to separate into single-species swarms, i.e., clusters. The data to be clustered are assigned to datoids which form a swarm on a twodimensional plane. A datoid can be imagined as a bird carrying a piece of data on its back. While swarming, this swarm divides into sub swarms moving over the plane and consisting of datoids carrying similar data. After swarming, these sub swarms of datoids can be grouped together as clusters.