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Diversity-Driven Widening of Hierarchical Agglomerative Clustering

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Advances in Intelligent Data Analysis XIV (IDA 2015)

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Abstract

In this paper we show that diversity-driven widening, the parallel exploration of the model space with focus on developing diverse models, can improve hierarchical agglomerative clustering. Depending on the selected linkage method, the model that is found through the widened search achieves a better silhouette coefficient than its sequentially built counterpart.

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References

  1. Akbar, Z., Ivanova, V.N., Berthold, M.R.: Parallel data mining revisited. better, not faster. In: Proceedings of the 11th International Symposium on Intelligent Data Analysis, pp. 23–34 (2012)

    Google Scholar 

  2. Caruana, R., Elhawary, M., Nguyen, N., Smith, C.: Meta clustering. In: 2006 Sixth International Conference on Data Mining, ICDM 2006, pp. 107–118. IEEE (2006)

    Google Scholar 

  3. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 1(2), 224–227 (1979)

    Article  Google Scholar 

  4. Day, W.H.E.: Optimal algorithms for comparing trees with labeled leaves. J. Classif. 2(1), 7–28 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  5. Graf, H.P., Cosatto, E., Bottou, L., Dourdanovic, I., Vapnik, V.: Parallel support vector machines: the cascade SVM. In: Advances in Neural Information Processing Systems, pp. 521–528 (2004)

    Google Scholar 

  6. Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., Tibshirani, R.: The Elements of Statistical Learning, vol. 2. Springer, New York (2009)

    Book  MATH  Google Scholar 

  7. Ivanova, V.N., Berthold, M.R.: Diversity-driven widening. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds.) IDA 2013. LNCS, vol. 8207, pp. 223–236. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  8. Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. Reports of the Faculty of Mathematics and Informatics, Faculty of Mathematics and Informatics (1987)

    Google Scholar 

  9. Kruskal, J.B., Wish, M.: Multidimensional Scaling, vol. 11. Sage, Beverly Hills (1978)

    Book  Google Scholar 

  10. Lichman, M.: UCI machine learning repository (2013)

    Google Scholar 

  11. Lozano, J.A., Larranaga, P.: Applying genetic algorithms to search for the best hierarchical clustering of a dataset. Pattern Recogn. Lett. 20(9), 911–918 (1999)

    Article  Google Scholar 

  12. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1. Cambridge university press, Cambridge (2008)

    Book  MATH  Google Scholar 

  13. Meinl, T.: Maximum-score diversity selection. Ph.D. thesis, University of Konstanz, July 2010

    Google Scholar 

  14. Olson, C.F.: Parallel algorithms for hierarchical clustering. Parallel Comput. 21(8), 1313–1325 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  15. Robinson, D.F., Foulds, L.R.: Comparison of phylogenetic trees. Math. Biosci. 53(12), 131–147 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  16. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  MATH  Google Scholar 

  17. Sampson, O., Berthold, M.R.: Widened KRIMP: better performance through diverse parallelism. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds.) IDA 2014. LNCS, vol. 8819, pp. 276–285. Springer, Heidelberg (2014)

    Google Scholar 

  18. Srivastava, A., Han, E.-H., Kumar, V., Singh, V.: Parallel formulations of decision-tree classification algorithms. In: Guo, Y., Grossman, R. (eds.) High Performance Data Mining, pp. 237–261. Springer, US (2002)

    Chapter  Google Scholar 

  19. Sundararajan, N., Saratchandran, P.: Parallel Architectures for Artificial Neural Networks: Paradigms and Implementations, 1st edn. IEEE Computer Society Press, Los Alamitos (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Chair for Bioinformatics and Information Mining, Department of CIS and Graduate School Chemical Biology (KoRS-CB), University of Konstanz, 78457, Konstanz, Germany

    Alexander Fillbrunn & Michael R. Berthold

Authors
  1. Alexander Fillbrunn
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  2. Michael R. Berthold
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Corresponding author

Correspondence to Alexander Fillbrunn .

Editor information

Editors and Affiliations

  1. Université de Saint-Etienne, Saint-Etienne, France

    Elisa Fromont

  2. Intelligent Systems Lab, University of Bristol Intelligent Systems Lab, Bristol, United Kingdom

    Tijl De Bie

  3. Informatics Section, Katholieke Universiteit Leuven, Leuven, Belgium

    Matthijs van Leeuwen

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Fillbrunn, A., Berthold, M.R. (2015). Diversity-Driven Widening of Hierarchical Agglomerative Clustering. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-24465-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24464-8

  • Online ISBN: 978-3-319-24465-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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