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Comparison of Proximity Measures: A Topological Approach

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Advances in Knowledge Discovery and Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 471))

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Abstract

In many application domains, the choice of a proximity measure affect directly the result of classification, comparison or the structuring of a set of objects. For any given problem, the user is obliged to choose one proximity measure between many existing ones. However, this choice depend on many characteristics. Indeed, according to the notion of equivalence, like the one based on pre-ordering, some of the proximity measures are more or less equivalent. In this paper, we propose a new approach to compare the proximity measures. This approach is based on the topological equivalence which exploits the concept of local neighbors and defines an equivalence between two proximity measures by having the same neighborhood structure on the objects.We compare the two approaches, the pre-ordering and our approach, to thirty five proximity measures using the continuous and binary attributes of empirical data sets.

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References

  1. Batagelj, V., Bren, M.: Comparing resemblance measures. Technical report, Proc. International Meeting on Distance Analysis, DISTANCIA 1992 (1992)

    Google Scholar 

  2. Batagelj, V., Bren, M.: Comparing resemblance measures. Journal of Classification 12, 73–90 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bouchon-Meunier, B., Rifqi, M., Bothorel, S.: Towards general measures of comparison of objects. Fuzzy Sets and Systems 84(2), 143–153 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  4. Clarke, K., Somerfield, P., Chapman, M.: On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted bray-curtis coefficient for denuded assemblages. Journal of Experimental Marine Biology and Ecology 330(1), 55–80 (2006)

    Article  Google Scholar 

  5. Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. In: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, p. 36. Society for Industrial and Applied Mathematics (2003)

    Google Scholar 

  6. Kim, J., Lee, S.: Tail bound for the minimal spanning tree of a complete graph. Statistics and Probability Letters 64(4), 425–430 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lerman, I.: Indice de similarité et préordonnance associée, Ordres. Travaux du séminaire sur les ordres totaux finis, Aix-en-Provence (1967)

    Google Scholar 

  8. Lesot, M.-J., Rifqi, M., Benhadda, H.: Similarity measures for binary and numerical data: a survey. IJKESDP 1(1), 63–84 (2009)

    Article  Google Scholar 

  9. Liu, H., Song, D., Rüger, S.M., Hu, R., Uren, V.S.: Comparing Dissimilarity Measures for Content-Based Image Retrieval. In: Li, H., Liu, T., Ma, W.-Y., Sakai, T., Wong, K.-F., Zhou, G. (eds.) AIRS 2008. LNCS, vol. 4993, pp. 44–50. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Malerba, D., Esposito, F., Monopoli, M.: Comparing dissimilarity measures for probabilistic symbolic objects. Series Management Information Systems 6, 31–40 (2002)

    Google Scholar 

  11. Mantel, N.: A technique of disease clustering and a generalized regression approach. Cancer Research 27, 209–220 (1967)

    Google Scholar 

  12. Noreault, T., McGill, M., Koll, M.: A performance evaluation of similarity measures, document term weighting schemes and representations in a boolean environment. In: Proceedings of the 3rd Annual ACM Conference on Research and Development in Information Retrieval, p. 76. Butterworth and Co. (1980)

    Google Scholar 

  13. Park, J., Shin, H., Choi, B.: Elliptic gabriel graph for finding neighbors in a point set and its application to normal vector estimation. Computer-Aided Design 38(6), 619–626 (2006)

    Article  Google Scholar 

  14. Preparata, F., Shamos, M.: Computational geometry: an introduction. Springer (1985)

    Google Scholar 

  15. Richter, M.: Classification and learning of similarity measures. In: Proceedings der Jahrestagung der Gesellschaft fur Klassifikation. Studies in Classification, Data Analysis and Knowledge Organisation. Springer (1992)

    Google Scholar 

  16. Schneider, J., Borlund, P.: Matrix comparison, part 1: Motivation and important issues for measuring the resemblance between proximity measures or ordination results. Journal American Society for Information Science and Technology 58(11), 1586–1595 (2007a)

    Article  Google Scholar 

  17. Schneider, J., Borlund, P.: Matrix comparison, part 2: Measuring the resemblance between proximity measures or ordination results by use of the mantel and procrustes statistics. Journal American Society for Information Science and Technology 58(11), 1596–1609 (2007b)

    Article  Google Scholar 

  18. Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating similarity measures: a large-scale study in the orkut social network. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, p. 684. ACM (2005)

    Google Scholar 

  19. Strehl, A., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: Workshop on Artificial Intelligence for Web Search (AAAI 2000), pp. 58–64 (2000)

    Google Scholar 

  20. Toussaint, G.: The relative neighbourhood graph of a finite planar set. Pattern Recognition 12(4), 261–268 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  21. Ward Jr., J.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)

    Article  MathSciNet  Google Scholar 

  22. Warrens, M.: Bounds of resemblance measures for binary (presence/absence) variables. Journal of Classification 25(2), 195–208 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhang, B., Srihari, S.: Properties of binary vector dissimilarity measures. In: Proc. JCIS Int’l Conf. Computer Vision, Pattern Recognition, and Image Processing. Citeseer (2003)

    Google Scholar 

  24. Zwick, R., Carlstein, E., Budescu, D.: Measures of similarity among fuzzy concepts: A comparative analysis. Int. J. Approx. Reason. 1(2), 221–242 (1987)

    Article  MathSciNet  Google Scholar 

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Correspondence to Djamel Abdelkader Zighed .

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Zighed, D.A., Abdesselam, R., Bounekkar, A. (2013). Comparison of Proximity Measures: A Topological Approach. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35855-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-35855-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35854-8

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