Multiscale Comparison of Temporal Patternsin Time-Series Medical Databases
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
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This paper presents a new, rough sets-based clusteringmethod that enables evaluation of simplicity of classification knowledge during the clustering procedure. The method iteratively refines equivalence relations so that they become more simple set of relations that give adequately coarse classification to the objects. At each step ofiteration, importance of the equivalence relation is evaluated on the basis of the newly introduced measure, indiscernibility degree. An indiscernibility degree is defined as a ratio of equivalence relations that classify the two objects into the same equivalence class. If an equivalence relation hasability to discern the two objects that have high indiscernibility degree, it is considered to perform too fine classification and then modified to regard them as indiscernible objects. The refinement is repeated decreasing the threshold level ofindiscernibility degree, and finally simple clusters can beobtained. Experimental results on the artificial data showed that iterative refinement of equivalence relation lead tosuccessful generation of coarse clusters that can be representedby simple knowledge.