BINCLUS: Nonhierarchical Clustering of Binary Data

Norman Cliff; Douglas J. McCormick; Judith L. Zatkin; Robert A. Cudeck; Linda M. Collins

doi:10.1207/s15327906mbr2102_4

BINCLUS: Nonhierarchical Clustering of Binary Data

Norman Cliff, Douglas J. McCormick, Judith L. Zatkin, Robert A. Cudeck, Linda M. Collins

Global Public Health

Research output: Contribution to journal › Article › peer-review

Abstract

BINCLUS is a clustering procedure designed for aggregating binary variables into relatively homogenous clusters. It uses any of several indices of binary association and operates by a variation on the “average linkage⃍ principle. It was tried out on a number of sets of artificial data and found to be extremely successful. With real data, where clusters are typically less clearly defined, two modifications were useful in clarifying the results. Results of using BINCLUS with two sets of real data are given.

Original language	English (US)
Pages (from-to)	201-227
Number of pages	27
Journal	Multivariate Behavioral Research
Volume	21
Issue number	2
DOIs	https://doi.org/10.1207/s15327906mbr2102_4
State	Published - Apr 1986

ASJC Scopus subject areas

Statistics and Probability
Experimental and Cognitive Psychology
Arts and Humanities (miscellaneous)

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title = "BINCLUS: Nonhierarchical Clustering of Binary Data",

abstract = "BINCLUS is a clustering procedure designed for aggregating binary variables into relatively homogenous clusters. It uses any of several indices of binary association and operates by a variation on the “average linkage⃍ principle. It was tried out on a number of sets of artificial data and found to be extremely successful. With real data, where clusters are typically less clearly defined, two modifications were useful in clarifying the results. Results of using BINCLUS with two sets of real data are given.",

author = "Norman Cliff and McCormick, {Douglas J.} and Zatkin, {Judith L.} and Cudeck, {Robert A.} and Collins, {Linda M.}",

note = "Funding Information: Requests for reprints should be sent to Norman Cliff, Department of Psycho'kogy, University of Southern California, Los Angeles, CA 90089-1061. This research was supported by the National Institute of Justice, grant number 79-NI-AX-0065 Copyright: Copyright 2015 Elsevier B.V., All rights reserved.",

year = "1986",

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doi = "10.1207/s15327906mbr2102_4",

language = "English (US)",

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AU - Cudeck, Robert A.

AU - Collins, Linda M.

N1 - Funding Information: Requests for reprints should be sent to Norman Cliff, Department of Psycho'kogy, University of Southern California, Los Angeles, CA 90089-1061. This research was supported by the National Institute of Justice, grant number 79-NI-AX-0065 Copyright: Copyright 2015 Elsevier B.V., All rights reserved.

PY - 1986/4

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N2 - BINCLUS is a clustering procedure designed for aggregating binary variables into relatively homogenous clusters. It uses any of several indices of binary association and operates by a variation on the “average linkage⃍ principle. It was tried out on a number of sets of artificial data and found to be extremely successful. With real data, where clusters are typically less clearly defined, two modifications were useful in clarifying the results. Results of using BINCLUS with two sets of real data are given.

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BINCLUS: Nonhierarchical Clustering of Binary Data

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