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. Most conceptual clustering methods are capable of generating hierarchical category structures; see Categorization for more information on hierarchy. Conceptual clustering is closely related to formal concept analysis, decision tree learning, and mixture model learning.
Other articles related to "conceptual clustering, conceptual":
... Conceptual clustering is a modern variation of the classical approach, and derives from attempts to explain how knowledge is represented ... or entities) are generated by first formulating their conceptual descriptions and then classifying the entities according to the descriptions ... Conceptual clustering developed mainly during the 1980s, as a machine paradigm for unsupervised learning ...
... What we wish to evaluate is the overall utility of grouping the objects into a particular hierarchical categorization structure ... Given a set of possible classification structures, we need to determine whether one is better than another ...
Famous quotes containing the word conceptual:
“Pure experience is the name I gave to the immediate flux of life which furnishes the material to our later reflection with its conceptual categories.”
—William James (18421910)