K-midranges clustering

  • J. Douglas Carroll
  • Anil Chaturvedi
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


We present a new clustering procedure called K-midranges clustering. K-midranges is analogous to the traditional K-Means procedure for clustering interval scale data. The K-midranges procedure explicitly optimizes a loss function based on the L, norm (defined as the limit of an Lp norm as p approaches infinity).


Continuous data Cluster analysis Groups Midrange K-means 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Carroll, J. D., & Chaturvedi, A. D. (1995). A General Approach to Clustering and Multidimensional Scaling of Two-way, Three-way, or Higher-way Data, in Geometric Representations of Perceptual Phenomena: Papers in Honor of Tarow Indow’s 70th Birthday. Luce, R. D., D’Zmura, M., Hoffman, D. D., Iverson, G., & Romney, A. K. (Eds.), Lawrence Erlbaum, 295 - 318.Google Scholar
  2. Chaturvedi, A. D., Carroll, J. D., Green, P., and Rotondo, J. A. (1997). A Feature based Approach to Market Segmentation via Overlapping K-centroids Clustering. Journal of Marketing Research, 34, 370–377CrossRefGoogle Scholar
  3. Chaturvedi, A. D., Paul E. Green, and J. Douglas Carroll (1996). Market Segmentation via K-modes clustering. Invited paper presented at the American Statistical Association conference held in Chicago.Google Scholar
  4. Hartigan, J. A. (1975). Clustering Algorithms, Wiley, New York.Google Scholar
  5. Hubert, L., & Phipps Arabie (1985). Comparing Partitions. Journal of Classification, 2, 193–218.CrossRefGoogle Scholar
  6. Jain, A. K. & Richard C. Dubes (1988), Algorithms for Clustering Data, Prentice- Hall, New Jersey.Google Scholar
  7. Kaufman, L. & Peter J. Rousseeuw (1989). Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York.Google Scholar
  8. MacQueen, J. B. (1967). Some Methods for Classification and Analysis of Multivariate Observations, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. LeCam, L. M., & Neyman, J. (Eds.), 1, 281–297.Google Scholar
  9. Mirkin, J. B. (1990). A Sequential Fitting Procedure for Linear Data Analysis Models. Journal of Classification, 7, 167–195.CrossRefGoogle Scholar
  10. Vinod, H. D. (1969). Integer Programming and Theory of Grouping. Journal of the American Statistical Association, 64, 506–519.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • J. Douglas Carroll
    • 1
  • Anil Chaturvedi
    • 2
  1. 1.Faculty of Management Management Education Center # 125Rutgers UniversityNewarkUSA
  2. 2.AT&T LaboratoriesMurray HillUSA

Personalised recommendations