Conceptual clustering is a machine learning paradigm for unsupervised classification developed mainly during the 1980s. It is distinguished from ordinary data clustering by generating a concept description for each generated class.
COBWEB is an incremental conceptual clustering system. It incrementally adds the objects into a classification tree. The attractive feature of incremental systems is that the knowledge is updated with each new observation.
Expectation-Maximization
Conceptual Clustering
Neural Network Approach
Methods of Clustering High-Dimensional Data ...
Numerical, statistical, and conceptual clustering.
Agglomerative vs. divisive.
Overlapping vs. disjoint clusters.
Another kind of clustering is conceptual clustering: two or more objects belong to the same cluster if this one defines a concept common to all that objects.
 See also: Clustering, Classification, Knowledge, Machine learning, Neural network
  
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