| Sumario: | Abstract. It is clear that nowadays analysis of complex systems is an important handicap for either Statistics) Artificial Intelligence, Information Systems, Data visualization,Describing the structure or obtaining knowledge from complex systems is known as a difficult task. It is innegable that the combination of Data analysis techniques (clustering among them) , inductive learning (knowledge-based systems), management of data bases and multidimensional graphical representation must produce benefits on this lineo Facing the automated knowledge discovery of ill~struciured domaíns raises some problems either from a machine learning or clustering point of view. Clusiering based on rules (CER) is a methodology developed in [9] with the aim of finding the structure of ill-structured dQmains. In our proposal, a combination of clustering and inductive learning is focussed to the problem of finding and interpreting special patterns (or concepts) from large data bases, in order to extract useful knowledge to represent real-world ·domains, giving better performance than traditional clustering algorithms or knowledge based systems approach. The scope of this paper is to present the methodology itself as well as to show how CER has several connection points with Knowledge Discovery of Data. Some applications are usa.d to illustrate this ideas.
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