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Constrained classification: The use of a priori information in cluster analysis. (English) Zbl 0554.62050

In many classification problems, one often possesses external and/or internal information concerning the objects or units to be analyzed which makes it appropriate to impose constraints on the set of allowable classifications and their characteristics. CONCLUS, or CONstrained CLUStering, is a new methodology devised to perform constrained classification in either an overlapping or nonoverlapping (hierarchical or nonhierarchical) manner. This paper initially reviews the related classification literature. A discussion of the use of constraints in clustering problems is then presented. The CONCLUS model and algorithm are described in detail, as well as their flexibility for use in various applications.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
65C05 Monte Carlo methods

Software:

Indclus
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References:

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