Algorithms for clustering data
Algorithms for clustering data
Information retrieval
Unsupervised learning of probabilistic concept hierarchies
Machine Learning and Its Applications
K-means Clustering Algorithm for Categorical Attributes
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
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
Approaches to conceptual clustering
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Data mining on multimedia data
Data mining on multimedia data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bounded Index for Cluster Validity
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Unsupervised cluster discovery using statistics in scale space
Engineering Applications of Artificial Intelligence
GUEST EDITORIAL: Intelligent data analysis in medicine-Recent advances
Artificial Intelligence in Medicine
Online clustering via finite mixtures of Dirichlet and minimum message length
Engineering Applications of Artificial Intelligence
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Conceptual k-means algorithm based on complex features
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
International Journal of Approximate Reasoning
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Case-based object recognition requires a general case of the object that should be detected. Real world applications such as the recognition of biological objects in images cannot be solved by one general case. A case-base is necessary to handle the great natural variations in the appearance of these objects. In this paper we will present how to learn a hierarchical case base of general cases. We present our conceptual clustering algorithm to learn groups of similar cases from a set of acquired structural cases. Due to its concept description it explicitly supplies for each cluster a generalized case and a measure for the degree of its generalization. The resulting hierarchical case base is used for applications in the field of case-based object recognition.