Models of incremental concept formation
Artificial Intelligence
The formation and use of abstract concepts in design
Concept formation knowledge and experience in unsupervised learning
Ordering effects in clustering
ML92 Proceedings of the ninth international workshop on Machine learning
Conceptual clustering with systematic missing values
ML92 Proceedings of the ninth international workshop on Machine learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Constraints on tree structure in concept formation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Paper: Learning and discovery from a clinical database: An incremental concept formation approach
Artificial Intelligence in Medicine
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Artificial Intelligence in Medicine
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Clustering is an important data analysis tool for discovering structure in data sets. Although research on conceptual clustering has produced algorithms showing significant advantages over earlier numerical ones, existing methods still present some limitations regarding applicability to biomedical domains. In this paper we describe ADAGIO, a conceptual clustering algorithm combining a low-cost preordering process with a breadth-first incremental control strategy that incorporates merging and splitting operators. Experimental evaluation indicated that the algorithm achieves a good balance between structure discovery performance and computational efficiency, and demonstrated the comparative effectiveness of its missing information handling process. ADAGIO is able to handle qualitative, quantitative and mixed-type data. An application example to a cancer domain is given, where the algorithm was able to suggest interesting epidemiological interpretations.