Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Machine learning: a guide to current research
Machine learning: a guide to current research
Techniques of design and DISCIPLE learning apprentice
International Journal of Expert Systems
An object-oriented modeling of the history of optimal retrievals
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A conceptual version of the K-means algorithm
Pattern Recognition Letters
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Discovery of Generalized Association Rules with Multiple Minimum Supports
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Improving similarity measures of histograms using smoothing projections
Pattern Recognition Letters
Flexible matching for noisy structural descriptions
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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An approach to concept learning from examples and concept learning by observation is presented that is based on a intuitive notion of conceptual distance between examples (concepts) and combines symbolical and numerical methods. The approach is based on the observation that very different examples generalize to an expression that is very far from each of them, while identical examples generalize to themselves. Following this idea the authors propose some domain-independent and intuitively justified estimates for the conceptual distance. A hierarchical conceptual clustering algorithm that groups objects so as to maximize the cohesiveness (a reciprocal of the conceptual distance) of the clusters is presented. It is shown that conceptual clustering can improve learning from complex examples describing objects and the relation between them.