Comparative Clustering for Contingent Collectives

  • Wayne L. Myers
  • Ganapati P. Patil
Part of the Environmental and Ecological Statistics book series (ENES, volume 6)


Clustering creates collectives of cases that have similar properties with a degree of distinctiveness. Clustering requires some composite measure of similarity or disparity, a criterion for conformity among collectives (linkage), and a strategy for configuring collectives. The collectives produced by a clustering method are conventionally called clusters. There are many methods of clustering, however, which typically differ to some degree in the groupings that result (Abonyi and Balaz 2007; Everitt et al. 2001; Kaufman and Rousseeuw 1990; Xu and Wunsch 2009). It is by comparing the collectives produced by different methods of clustering that one can gain insight from inconsistencies and have some confidence relative to consistencies. We call this comparative or complementary clustering and we use the term contingents (groups from groupings) for collectives of cases that emerge from this compound approach using cross-tabulations. Preliminary prioritization can be done among contingents and then progress to comparisons within contingents so that the computational complexities of comprehensive comparisons can be controlled.


Hierarchical Cluster Geographic Information System Gain Insight Principal Component Axis Principal Component Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abonyi J, Balaz F (2007) Cluster analysis for data mining and system identification. Birkhauser, BerlinMATHGoogle Scholar
  2. Basu S, Davidson I, Wagstaff K (2009) Constrained clustering: advances in algorithms, theory, and applications. Chapman & Hall/CRC, Boca Raton, FLMATHGoogle Scholar
  3. Breiman L, Freidman J, Olsen R, Stone C (1998) Classification and regression trees (CART). Chapman & Hall/CRC, Boca Raton, FLGoogle Scholar
  4. Everitt B, Landau S, Leese M (2001) Cluster analysis. Arnold, LondonMATHGoogle Scholar
  5. Fielding A (2007) Cluster and classification techniques for the biosciences. Cambridge University Press, CambridgeGoogle Scholar
  6. Gan G, Ma C, Ma C, Wu J (2007) Data clustering: theory, algorithms, and applications. SIAM, Philadelphia, PAMATHCrossRefGoogle Scholar
  7. Halgamuge S, Wang L (eds) (2005) Classification and clustering for knowledge discovery. Springer, DordrechtMATHGoogle Scholar
  8. Hardle W, Simar L (2007) Applied multivariate statistical analysis. Springer, BerlinGoogle Scholar
  9. Kaufman L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkCrossRefGoogle Scholar
  10. Long B, Zhang Z, Yu P (2010) Relational data clustering: models, algorithms and applications. Chapman & Hall/CRC, Boca Raton, FLMATHGoogle Scholar
  11. Lumley T (2010) Complex surveys: a guide to analysis using R. Wiley, Hoboken, NJGoogle Scholar
  12. Mirkin B (2005) Clustering for data mining: a data recovery approach. Chapman & Hall/CRC, Boca Raton, FLMATHCrossRefGoogle Scholar
  13. Myers W, Patil GP (2006) Pattern-based compression of multi-band image data for landscape analysis. Springer, New YorkMATHGoogle Scholar
  14. Myers W, McKenney-Easterling M, Hychka K, Griscom B, Bishop J, Bayard A, Rocco G, Brooks R, Constantz G, Patil GP, Taillie C (2006) Contextual clustering for configuring collaborative conservation of watershed in the Mid-Atlantic Highlands. Environ Ecol Stat 13(4):391–407MathSciNetCrossRefGoogle Scholar
  15. Podani J (2000) Introduction to the exploration of multivariate biological data. Backhuys, LeidenGoogle Scholar
  16. Xu R, Wunsch D (2009) Clustering. Wiley, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Wayne L. Myers
    • 1
  • Ganapati P. Patil
    • 2
  1. 1.Penn State Institutes of Energy and EnvironmentPennsylvania State UniversityUniversity ParkUSA
  2. 2.Center for Statistical Ecology and Environmental Statistics Department of StatisticsPennsylvania State UniversityUniversity ParkUSA

Personalised recommendations