Abstract
This article proposes a supervised approach to evaluate the contribution of explanatory variables to a clustering. The main idea is to learn to predict the instance membership to the clusters using each individual variable. All variables are then sorted with respect to their predictive power, which is measured using two evaluation criteria, i.e. accuracy (ACC) or Adjusted Rand Index (ARI). Once the relevant variables which contribute to the clustering discrimination have been determined, we filter out the redundant ones thanks to a supervised method. The aim of this work is to help end-users to easily understand a clustering of high-dimensional data. Experimental results show that our proposed method is competitive with existing methods from the literature.
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References
Dy, J.G., Brodley, C.E.: Feature selection for unsupervised learning. J. Mach. Learn. Res. 5, 845–889 (2004)
Liu, P., Zhu, J., Liu, L., Li, Y., Zhang, X.: Application of feature selection for unsupervised learning in prosecutors’ office. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3614, pp. 35–38. Springer, Heidelberg (2005)
Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)
Vesanto, J., Ahola, J.: Hunting for correlations in data using the self-organizing map. In: Proceeding of the International ICSC Congress on Computational Intelligence Methods and Applications (CIMA 1999), pp. 279–285. ICSC Academic Press (1999)
Guérif, S., Bennani, Y., Janvier, E.: μ-SOM: Weighting features during clustering. In: Proceeding of the 5th Workshop on Self-organizing Maps (WSOM 2005), pp. 397–404 (2005)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2-3), 107–145 (2001)
Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2(1), 193–218 (1985)
Larsen, B., Aone, C.: Fast and effective text mining using linear-time document clustering. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ICDM), pp. 16–22. ACM, New York (1999)
Alok, A.K., Sriparna, S., Ekbal, A.: A min-max distance based external cluster validity index: MMI. In: HIS, pp. 354–359. IEEE (2012)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1(2), 224–227 (1979)
Rousseeuw, P.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. (1), 53–65 (1987)
Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3(3), 32–57 (1973)
Halkidi, M., Vazirgiannis, M., Batistakis, Y.: Quality scheme assessment in the clustering process. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 265–276. Springer, Heidelberg (2000)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)
Raftery, A.: A note on Bayes factors for log-linear contingency table models with vague prior information. Journal of the Royal Statistical Society, 249–250 (1986)
Santos, J.M., Embrechts, M.: On the use of the adjusted rand index as a metric for evaluating supervised classification. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 175–184. Springer, Heidelberg (2009)
Blake, C.L., Merz, C.J.: Uci repository of machine learning databases (1998)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Cam, L.M.L., Neyman, J. (eds.) Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)
Celeux, G., Martin-Magniette, M.L., Maugis, C., Raftery, A.E.: Comparing model selection and regularization approaches to variable selection in model-based clustering. Journal de la Société Française de Statistique (2014)
Arthur, D., Vassilvitskii, S.: K-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027–1035 (2007)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth International Group (1984)
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Ismaili, O.A., Lemaire, V., Cornuéjols, A. (2014). A Supervised Methodology to Measure the Variables Contribution to a Clustering. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_20
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DOI: https://doi.org/10.1007/978-3-319-12637-1_20
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