Perspectives of self-adapted self-organizing clustering in organic computing

Clustering tasks occur for various different application domains including very large data streams e.g. for robotics and life science, different data formats such as graphs and profiles, and a multitude of different objectives ranging from statistical motivations to data driven quantization errors. Thus, there is a need for efficient any-time self-adaptive models and implementations. The focus of this contribution is on clustering algorithms inspired by biological paradigms which allow to transfer ideas of organic computing to the important task of efficient clustering. We discuss existing methods of adaptivity and point out a taxonomy according to which adaptivity can take place. Afterwards, we develop general perspectives for an efficient self-adaptivity of self-organizing clustering.