numerical clustering, object oriented perspective, BETA

Results achieved by combination of the hierarchical and partitional clustering are described in this paper. Proper design and object-oriented approach bring new possibilities of the various combinations of the strategies in numerical clustering. In our progress hierarchical clustering is used for production of the hypothetical classification structure. In the following new clusters are compared with this structure using partitional clustering. Numerical results gained by this process may be used for making a diagnosis. Actual state of the hierarchical clustering is stored and retrieved by using persistent store and persistent objects. Described approach was tested on medical data. The whole application is developed using the Mjolner BETA System and the BETA language for its rich facilities of the conceptual modeling.

Results achieved by combination of the hierarchical and partitional clustering are described in this paper. Proper design and object-oriented approach bring new possibilities of the various combinations of the strategies in numerical clustering. In our progress hierarchical clustering is used for production of the hypothetical classification structure. In the following new clusters are compared with this structure using partitional clustering. Numerical results gained by this process may be used for making a diagnosis. Actual state of the hierarchical clustering is stored and retrieved by using persistent store and persistent objects. Described approach was tested on medical data. The whole application is developed using the Mjolner BETA System and the BETA language for its rich facilities of the conceptual modeling.