Abstract
Clustering techniques are important for knowledge acquisition. Traditionally, numerical clustering methods have been viewed in opposition to conceptual clustering methods developed in Artificial Intelligence. Numerical techniques emphasize the determination of homogeneous clusters but provide low-level descriptions of clusters. A conceptual approach is more concerned with high-level, i.e., more understandable descriptions of classes. In this paper, we propose a hybrid numericsymbolic method that integrates an extended version of the K-means algorithm for cluster determination and a complementary conceptual characterization algorithm for cluster description.
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