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An introduction to support Vector Machines: and other kernel-based learning methods
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Relational Data Mining
Inductive Logic Programming: Techniques and Applications
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Relational Data Mining
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Relational Data Mining
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
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Handbook of Granular Computing
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Rough Granular Computing in Knowledge Discovery and Data Mining
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International Journal of Approximate Reasoning
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Artificial Intelligence
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International Journal of Approximate Reasoning
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Information Sciences: an International Journal
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Fundamenta Informaticae
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In this paper, we introduce a method for measuring similarity of objects of a relational database (relational objects, in short). We also propose and investigate an algorithm SC for classification of relational objects. The task of classification is carried out based on similarity of the objects to predefined classes. An object to be classified is assigned to the class to which it is most similar. A similarity of an object to a class is understood as its similarity to a class representative. Several methods for computing the class representative are proposed. We test the algorithm on real and artificial databases. We compare results obtained by the algorithm with those obtained by other algorithms known from the literature. We also present our approach in the context of granular computing.