Incremental semi-supervised clustering in a data stream with a flock of agents

Today, in many clustering applications we deal with a large amount of data that are delivered in form of data streams. To be able to face the problem of analyzing the data as soon as they are produced, we need to build models that can be incrementally updated. This paper presents an adaptation of a bio-inspired algorithm that dynamically creates and visualizes groups of data, to data stream clustering. We introduce a merge operator that can summarize a group of data and a split operator that uses information of a very small set of supervised data and permits to adapt the clustering to a change in the data stream.