Models of incremental concept formation
Artificial Intelligence
Reasoning and revision in hybrid representation systems
Reasoning and revision in hybrid representation systems
Concept formation in structured domains
Concept formation knowledge and experience in unsupervised learning
Spatial analogy and subsumption
ML92 Proceedings of the ninth international workshop on Machine learning
Computer and Robot Vision
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Representation for Discovery of Protein Motifs
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Extending case-based reasoning by discovering and using image features in IVF
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Data mining for knowledge acquisition in engineering design
Data mining for design and manufacturing
Finding Patterns in Three-Dimensional Graphs: Algorithms and Applications to Scientific Data Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering Matrix Attachment Regions (MARs) in genomic databases
ACM SIGKDD Explorations Newsletter
Distinctive Features of Minimization of a Risk Functional in Mass Data Sets
Cybernetics and Systems Analysis
Analysis of three-dimensional protein images
Journal of Artificial Intelligence Research
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An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which object's described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map.