Models of incremental concept formation
Artificial Intelligence
Reasoning and revision in hybrid representation systems
Reasoning and revision in hybrid representation systems
Artificial Intelligence Review - Special issue on lazy learning
Reasoning with complex cases
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Similarity Measures for Structured Representations
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
A Two Layer Case-Based Reasoning Architecture for Medical Image Understanding
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Structural Similarity and Adaptation
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Structured Cases, Trees and Efficient Retrieval
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Similarity Measures for Object-Oriented Case Representations
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
A Study on Competence-Preserving Case Replacing Strategies in Case-Based Reasoning
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Merge Strategies for Multiple Case Plan Replay
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Learning Feature Weights for CBR: Global versus Local
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Categorizing Case-Base Maintenance: Dimensions and Directions
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Experiences Using Clustering and Generalizations for Knowledge Discovery in Melanomas Domain
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Case-Based Reasoning and the Statistical Challenges
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Editorial: Recent advances in data mining
Engineering Applications of Artificial Intelligence
Concepts for novelty detection and handling based on a case-based reasoning process scheme
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Classification of melanomas in situ using knowledge discovery with explained case-based reasoning
Artificial Intelligence in Medicine
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Case-base maintenance typically involves the addition, removal or revision of cases, but can also include changes to the retrieval knowledge. In this paper, we consider the learning of the retrieval knowledge (organization) as well as the prototypes and the cases as case-based maintenance. We address this problem based on cases that have a structural case representation. Such representations are common in computer vision and image interpretation, building design, timetabling or gene-nets. In this paper we propose a similarity measure for an attributed structural representation and an algorithm that incrementally learns the organizational structure of a case base. This organization schema is based on a hierarchy and can be updated incrementally as soon as new cases are available. The tentative underlying conceptual structure of the case base is visually presented to the user. We describe two approaches for organizing the case base. Both are based on approximate graph subsumption. The first approach is based on a divide-and-conquer strategy whereas the second one is based on a split-and-merge strategy which better allows to fit the hierarchy to the actual structure of the application but takes more complex operations.