An Overview of Artificial Life Approaches for Clustering

Abstract

Recently, artificial life approaches for clustering have been proposed. However, the research on artificial life is mainly the simulation of systems based on models for real life. In addition to that artificial life methods have been utilized to solve optimization problems. This paper gives a short overview of artificial life and its applications in general. From this starting point we will focus on artificial life approaches used for clustering. These approaches are characterized by the fact that solutions are emergent rather than predefined and preprogrammed. The data is seen as active rather than passive objects. New data can be added incrementally to the system. We will present existing concepts for clustering with artificial life and highlight their differences and strengths.