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Clustering in an Object-Oriented Environment

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  • Struyf, Anja
  • Hubert, Mia
  • Rousseeuw, Peter

Abstract

This paper describes the incorporation of seven stand-alone clustering programs into S-PLUS, where they can now be used in a much more flexible way. The original Fortran programs carried out new cluster analysis algorithms introduced in the book of Kaufman and Rousseeuw (1990). These clustering methods were designed to be robust and to accept dissimilarity data as well as objects-by-variables data. Moreover, they each provide a graphical display and a quality index reflecting the strength of the clustering. The powerful graphics of S-PLUS made it possible to improve these graphical representations considerably. The integration of the clustering algorithms was performed according to the object-oriented principle supported by S-PLUS. The new functions have a uniform interface, and are compatible with existing S-PLUS functions. We will describe the basic idea and the use of each clustering method, together with its graphical features. Each function is briefly illustrated with an example.

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  • Struyf, Anja & Hubert, Mia & Rousseeuw, Peter, 1997. "Clustering in an Object-Oriented Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 1(i04).
  • Handle: RePEc:jss:jstsof:v:001:i04
    DOI: http://hdl.handle.net/10.18637/jss.v001.i04
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    1. M. Mufakharul Islam, 1986. "Discussion," The Indian Economic & Social History Review, , vol. 23(2), pages 217-226, June.
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    4. Wen, Xuanhao & Cao, Huajun & Li, Hongcheng & Zheng, Jie & Ge, Weiwei & Chen, Erheng & Gao, Xi & Hon, Bernard, 2022. "A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques," Energy, Elsevier, vol. 255(C).
    5. Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
    6. Karpinska, Lilia & Śmiech, Sławomir, 2021. "Breaking the cycle of energy poverty. Will Poland make it?," Energy Economics, Elsevier, vol. 94(C).
    7. Jesus Gonzalez-Feliu & Joelle Morana & Josep-Maria Salanova Grau & Tai-Yu Ma, 2013. "Design And Scenario Assessment For Collaborative Logistics And Freight Transport Systems," Articles, International Journal of Transport Economics, vol. 40(2).
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    10. Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
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    12. Albrecht Kauffmann, 2011. "Wirkung kommunaler Investitionen in die Tourismusinfrastruktur am Beispiel Sachsens," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 31(1), pages 57-73, June.
    13. Mohiuddin Ahmed, 2018. "Collective Anomaly Detection Techniques for Network Traffic Analysis," Annals of Data Science, Springer, vol. 5(4), pages 497-512, December.
    14. Kauffmann, Albrecht, 2012. "Delineation of City Regions Based on Commuting Interrelations: The Example of Large Cities in Germany," IWH Discussion Papers 4/2012, Halle Institute for Economic Research (IWH).
    15. Kim, Jaejik & Billard, L., 2011. "A polythetic clustering process and cluster validity indexes for histogram-valued objects," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2250-2262, July.
    16. Jörg Weking & Michael Mandalenakis & Andreas Hein & Sebastian Hermes & Markus Böhm & Helmut Krcmar, 2020. "The impact of blockchain technology on business models – a taxonomy and archetypal patterns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 285-305, June.

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