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ARTICLE

Comparing clusterings: an axiomatic view

ABSTRACT

This paper views clusterings as elements of a lattice. Distances between clusterings are analyzed in their relationship to the lattice. From this vantage point, we first give an axiomatic characterization of some criteria for comparing clusterings, including the variation of information and the unadjusted Rand index. Then we study other distances between partitions w.r.t these axioms and prove an impossibility result: there is no "sensible" criterion for comparing clusterings that is simultaneously (1) aligned with the lattice of partitions, (2) convexely additive, and (3) bounded.

References

  1. Ben-Hur, A., Elisseeff, A., & Guyon, I. (2002). A stability based method for discovering structure in clustered data. Pacific Symposium on Biocomputing (pp. 6--17).]]Google ScholarGoogle ScholarOpenURL University of Bath
  2. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. Wiley.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
  3. Fowlkes, E. B., & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78, 553--569.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
  4. Golumbic, M. (1980). Algorithmic graph theory and perfect graphs. Academic Press, New York.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
  5. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193--218.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
  6. Kleinberg, J. (2002). An impossibility theorem for clustering. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.]]Google ScholarGoogle ScholarOpenURL University of Bath
  7. Meilǎ, M. (2003). Comparing clusterings by the variation of information. Proceedings of the Sixteenth Annual Conference ofn Computational Learning Theory (COLT). Springer.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
  8. Mirkin, B. (1996). Mathematical classification and clustering. Kluwer Academic Press.]]Google ScholarGoogle ScholarOpenURL University of Bath
  9. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66, 846--850.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
  10. Rènyi, A. (1970). Probability theory. North-Holland.]]Google ScholarGoogle ScholarOpenURL University of Bath
  11. Stanley, R. P. (1997). Enumerative combinatorics. Cambridge University Press.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
  12. van Dongen, S. (2000). Performance criteria for graph clustering and Markov cluster experiments (Technical Report INS-R0012). Centrum voor Wiskunde en Informatica.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
  13. Wallace, D. L. (1983). Comment. Journal of the American Statistical Association, 78, 569--576.]]Google ScholarGoogle ScholarOpenURL University of Bath

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  1. Comparing clusterings

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      1. Ben-Hur, A., Elisseeff, A., & Guyon, I. (2002). A stability based method for discovering structure in clustered data. Pacific Symposium on Biocomputing (pp. 6--17).]]Google ScholarGoogle ScholarOpenURL University of Bath
      2. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. Wiley.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
      3. Fowlkes, E. B., & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78, 553--569.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
      4. Golumbic, M. (1980). Algorithmic graph theory and perfect graphs. Academic Press, New York.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
      5. Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193--218.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
      6. Kleinberg, J. (2002). An impossibility theorem for clustering. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.]]Google ScholarGoogle ScholarOpenURL University of Bath
      7. Meilǎ, M. (2003). Comparing clusterings by the variation of information. Proceedings of the Sixteenth Annual Conference ofn Computational Learning Theory (COLT). Springer.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
      8. Mirkin, B. (1996). Mathematical classification and clustering. Kluwer Academic Press.]]Google ScholarGoogle ScholarOpenURL University of Bath
      9. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66, 846--850.]]Google ScholarGoogle ScholarCross RefCross RefOpenURL University of Bath
      10. Rènyi, A. (1970). Probability theory. North-Holland.]]Google ScholarGoogle ScholarOpenURL University of Bath
      11. Stanley, R. P. (1997). Enumerative combinatorics. Cambridge University Press.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
      12. van Dongen, S. (2000). Performance criteria for graph clustering and Markov cluster experiments (Technical Report INS-R0012). Centrum voor Wiskunde en Informatica.]] Google ScholarGoogle ScholarDigital LibraryDigital LibraryOpenURL University of Bath
      13. Wallace, D. L. (1983). Comment. Journal of the American Statistical Association, 78, 569--576.]]Google ScholarGoogle ScholarOpenURL University of Bath
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