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Cluster Analysis

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Encyclopedia of Operations Research and Management Science

Introduction

Cluster analysis is a generic term for various procedures that are used objectively to group entities based on their similarities and differences. In applying these procedures, the objective is to group the entities (elements, items, objects, etc.) into mutually exclusive clusters so that elements within each cluster are relatively homogeneous in nature while the clusters themselves are distinct. The key purposes of cluster analysis are reduction of data, data exploration, determination of natural groups, prediction based on groups, classification, model fitting, generation and testing of hypotheses (Everitt 1993; Aldenderfer and Blashfield 1984; Lorr 1983).

Due to the importance of clustering in different disciplines such as psychology, zoology, botany, sociology, artificial intelligence and information retrieval, a variety of other names have been used to refer to such techniques: Q-analysis, typology, grouping, clumping, classification, numerical taxonomy, and...

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References

  • Abonyi, J., & Feil, B. (2007). Cluster analysis for data mining and system identification. Basel: Birkhäuser.

    Google Scholar 

  • Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. California: Sage Publications.

    Google Scholar 

  • Allison, S. T., Jordan, A. M. R., & Yeatts, C. E. (1992). A cluster-analytic approach toward identifying the structure and content of human decision making. Human Relations, 45, 49–73.

    Article  Google Scholar 

  • Anderberg, M. R. (1973). Cluster analysis for applications. New York: Academic.

    Google Scholar 

  • Aronson, J. E., & Klein, G. (1989). A clustering algorithm for computer-assisted process organization. Decision Sciences, 20, 730–745.

    Article  Google Scholar 

  • Balas, E. (1965). An additive algorithm for solving linear programs with zero–one variables. Operations Research, 13, 517–546.

    Article  Google Scholar 

  • Cattell, R. B. (1952). Factor analysis: An introduction and manual for the psychologist and social scientist. New York: Harper.

    Google Scholar 

  • Cormack, R. M. (1971). A review of classification. Journal of the Royal Statistical Society (Series A), 134, 321–367.

    Article  Google Scholar 

  • DeSouza, R. B. R., & Bell, R. (1991). A tool cluster based strategy for the management of cutting tools in flexible manufacturing systems. Journal of Operations Management, 10, 73–91.

    Article  Google Scholar 

  • Everitt, B. (1993). Cluster analysis (3rd ed.). New York: Halsted Press.

    Google Scholar 

  • Gengerelli, J. A. (1963). A method for detecting subgroups in a population and specifying their membership. Journal of Psychology, 5, 456–468.

    Google Scholar 

  • Good, I. J. (1977). The botryology of botryology. In J. Van Ryzin (Ed.), Classification and clustering. New York: Academic.

    Google Scholar 

  • Gower, J. C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.

    Article  Google Scholar 

  • Gower, J. C., & Ross, G. J. S. (1969). Minimum spanning trees and single linkage cluster analysis. Journal of the Royal Statistical Society. Series C (Applied Statistics), 18, 54–64.

    Google Scholar 

  • Hair, J. F., Jr., Anderson, R. E., & Tatham, R. L. (1987). Multivariate data analysis (2nd ed.). New York: Macmillan.

    Google Scholar 

  • Hartigan, J. A. (1975). Clustering algorithms. New York: Wiley.

    Google Scholar 

  • Hirschberg, J. G., Maasoumi, E., & Slottje, D. J. (1991). Cluster analysis for measuring welfare and quality of life across countries. Journal of Econometrics, 50, 131–150.

    Article  Google Scholar 

  • Hodgkinson, G. P., Padmore, J., & Tomes, A. E. (1991). Mapping consumer’s cognitive structures: A comparison of similarity trees with multidimensional scaling and cluster analysis. European Journal of Marketing, 25, 41–60.

    Article  Google Scholar 

  • Homayoun, A. S. (1984). The use of cluster analysis in analyzing large engineering records collection. Records Management Quarterly, October, 22–25.

    Google Scholar 

  • Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Karimi, J. (1986). An automated software design methodology using CAPO. Journal of Management Information Systems, 3, 71–100.

    Article  Google Scholar 

  • Klein, G., & Aronson, J. E. (1991). Optimal clustering: A model and method. Naval Research Logistics, 38, 447–461.

    Article  Google Scholar 

  • Klein, G., Beck, P. O., & Konsynski, B. R. (1988). Computer aided process structuring via mixed integer programming. Decision Sciences, 19, 750–761.

    Article  Google Scholar 

  • Kuo, C.-C., Glover, F., & Dhir, K. S. (1993). Analyzing and modeling the maximum diversity problem by zero–one programming. Decision Sciences, 24, 1171–1185.

    Article  Google Scholar 

  • Lorr, M. (1983). Cluster analysis for social scientists. California: Jossey-Bass Publishers.

    Google Scholar 

  • Matula, D. W. (1977). Graph theoretic techniques for cluster analysis algorithms. In J. Van Ryzin (Ed.), Classification and clustering. New York: Academic Press.

    Google Scholar 

  • McRae, D. J. (1971). MICKA, A FORTRAN IV iterative K-means cluster analysis program. Behavioural Science, 16, 423–424.

    Google Scholar 

  • Mulvey, J., & Crowder, H. (1979). Cluster analysis: An application of lagrangian relaxation. Management Science, 25, 329–340.

    Article  Google Scholar 

  • Needham, R. M. (1967). Automatic classification in linguistics. The Statistician, 17, 45–54.

    Article  Google Scholar 

  • Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20, 134–148.

    Article  Google Scholar 

  • Rohlf, F. J. (1974). Graphs implied by the Jardine-Sibson overlapping clustering methods. Journal of the American Statistical Association, 69, 705–710.

    Google Scholar 

  • Romesburg, H. (1984). Cluster analysis for researchers. Belmont, CA: Lifetime Learning Publications.

    Google Scholar 

  • Sneath, P. H. A., & Sokal, R. R. (1973). Principles of numerical taxonomy. San Francisco: W.H. Freeman.

    Google Scholar 

  • Spisak, A. W. (1992). Cluster analysis as a quality management tool. Quality Progress, 25, 33–38.

    Google Scholar 

  • Weitz, R. R., & Lakshminarayanan, S. (1997). An empirical comparison of heuristic and graph theoretic methods for creating maximally diverse groups, VLSI design, and exam scheduling. Omega, 25, 473–482.

    Article  Google Scholar 

  • Weitz, R. R., & Lakshminarayanan, S. (1998). An empirical comparison of heuristic methods for creating maximally diverse groups. Journal of the Operational Research Society, 49, 635–646.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Management Information Systems, The University of Georgia, Brooks Hall 307, Athens, 30602-6273, USA

    Jay E. Aronson

  2. Department of Information Systems and Operations Management, The University of North Carolina at Greensboro, 26165, Greensboro, 27402-6165, NC, USA

    Lakshmi S. Iyer

Authors
  1. Jay E. Aronson
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  2. Lakshmi S. Iyer
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Corresponding author

Correspondence to Jay E. Aronson .

Editor information

Editors and Affiliations

  1. Robert H. Smith School of Business, University of Maryland, College Park, MD, USA

    Saul I. Gass

  2. Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, MD, USA

    Michael C. Fu

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© 2013 Springer Science+Business Media New York

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Aronson, J.E., Iyer, L.S. (2013). Cluster Analysis. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_119

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  • DOI: https://doi.org/10.1007/978-1-4419-1153-7_119

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-1137-7

  • Online ISBN: 978-1-4419-1153-7

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