##
**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
PDF
BibTeX
XML
Cite

\textit{W. S. DeSarbo} and \textit{V. Mahajan}, Psychometrika 49, 187--215 (1984; Zbl 0554.62050)

Full Text:
DOI

### References:

[1] | Carroll, J. D. and Pruzansky, S. (1975). Fitting of hierarchical tree structure (HTS) models, mixtures of HTS models, and hybrid models, via mathematical programming and alternating least squares,Working Paper, Bell Laboratories, Murray Hill, N.J. |

[2] | Carroll, J. D. and Arabie, P. (1979). INDCLUS: A three-way approach to clustering. Presented at theMeeting of the Psychometric Society, Monterey, Cal. |

[3] | DeSarbo, W. S., Carroll, J. D., Clark, L. A., and Green, P. E. (1982). Synthesized Clustering: A method for amalgamating alternative clustering bases with differential weighting of variables,Working Paper, Bell Laboratories. · Zbl 0594.62067 |

[4] | Carroll, J. D., and Chang, J. J. (1972).IDIOSCAL: A generalization of INDSCAL allowingIDIOsyncratic reference systems as well as an analytic approximation to INDSCAL. Paper presented at the meetings of the Psychometric Society, Princeton, N.J. |

[5] | Fowlkes, E. B., Gnanadesikan, R., and Kettenring, J. R. (1982). Variable selection in clustering,Working Paper, Bell Laboratories, Murray Hill, N.J. |

[6] | Harshman, R. A. (1972). Determination and proof of minimum uniqueness conditions for PARAFAC 1.U.C.L.A. Working Papers in Phonetics, 22. |

[7] | Klastorin, T. D. (1973). A clustering approach to systems design,Unpublished Manuscript, University of Texas at Austin. |

[8] | Mallows, C. L. (1982). Personal communication. |

[9] | Perruchet, C. (1979). Classification sous constrainte de contiguite continue (Application aux sciences de la terre).Thesis, Paris. |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.