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Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017) pp 303–308Cite as

Comparison Between k-Means and k-Medoids for Mixed Variables Clustering

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Abstract

This paper compares the performance of k-means and k-medoids in clustering objects with mixed variables. The k-means initially means for clustering objects with continuous variables as it uses Euclidean distance to compute distance between objects. While, k-medoids has been designed suitable for mixed type variables especially with PAM (partition around medoids). By using a mixed variables data set on a modified cancer data, we compared k-means and k-medoids on internal validity set up in R package. The result indicates that k-medoids is a good clustering option when the measured variables are mixed with different types.

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References

  1. Hennig, C.: Clustering strategy and method selection. In: Hennig, C., Meila, M., Murtagh, F., and Rocci, R. (eds.) Handbook of Cluster Analysis, pp. 1–34. Chapman & Hall/CRC Press (2015)

    Google Scholar 

  2. Macqueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  3. Cui, X., Zhu, P., Yang, X., Li, K., Ji, C.: Optimized big data K-means clustering using MapReduce. J. Supercomput. 70, 1249–1259 (2014). https://doi.org/10.1007/s11227-014-1225-7

  4. Tzortzis, G., Likas, A.: The MinMax k-means clustering algorithm. Pattern Recognit. 47, 2505–2516 (2014)

    Article  Google Scholar 

  5. Huang, Z.: Clustering large data sets with mixed numeric and categorical values. In: In the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 21–34 (1997)

    Google Scholar 

  6. Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. In: Dodge, Y. (ed.) Statistical data analysis based on the L1 norm and related methods, pp. 405–416. Faculty of Mathematics and Informatics, North-Holland (1987)

    Google Scholar 

  7. Jin, X., Han, J.: K-medoids clustering (2010). https://doi.org/10.1007/978-0-387-30164-8_426

  8. Kaufman, L., Rousseeuw, P.J.: Partitioning around medoids (program PAM). In: Finding Groups in Data: An Introduction to Clustering Analysis, pp. 68–125 (1990)

    Google Scholar 

  9. Hu, X., Xu, L.: Investigation on several model selection criteria for determining the number of cluster. Neural Inf. Process. Rev. 4, 1–10 (2004)

    Google Scholar 

  10. Podani, J.: Extending Gower’s general coefficient of similarity to ordinal characters. Taxon 48, 331–340 (1999)

    Article  Google Scholar 

  11. Sindik, J.: Two aspects of bias in multivariate studies: mixing specific with general concepts and “comparing apples and oranges”. J. Sport. Sci. Med. 3, 23–29 (2014)

    Google Scholar 

  12. Lourenço, F., Lobo, V., Bação, F.: Binary-based similarity measures for categorical data and their application in Self-Organizing Maps. Measurement, 1–18 (2004)

    Google Scholar 

  13. Hennig, C.: Package “fpc,” (2018)

    Google Scholar 

  14. Brock, G., Pihur, V., Datta, S.S., Datta, S.S.: clValid : an R package for cluster validation. J. Stat. Softw. 25, 1–28 (2008). doi:citeulike-article-id:2574494

    Google Scholar 

  15. Fernandes, K., Cardoso, J.S., Fernandes, J.: Transfer learning with partial observability applied to cervical cancer screening. In: Iberian Conference on Pattern Recognition and Image Analysis, pp. 243–250. © Springer International Publishing (2017)

    Google Scholar 

  16. Ezat, S., Puteh, W., Norin-Rahayu, S., Noor, S., Syed, A., Azhar, S., Aljunid, S.M., Science, Q., Lumpur, K., Latiff, J.Y.: HPV positivity and its’ influencing factors among invasive cervical cancer women in Malaysia. Int. J. Public Health 1, 13–22 (2011)

    Article  Google Scholar 

  17. Roura, E., Castellsagué, X., Pawlita, M., Travier, N., Waterboer, T., Margall, N., Bosch, F.X., De Sanjosé, S., Dillner, J., Gram, I.T., Tjønneland, A., Munk, C., Pala, V., Palli, D., Khaw, K.T., Barnabas, R. V., Overvad, K., Clavel-Chapelon, F., Boutron-Ruault, M.C., Fagherazzi, G., Kaaks, R., Lukanova, A., Steffen, A., Trichopoulou, A., Trichopoulos, D., Klinaki, E., Tumino, R., Sacerdote, C., Panico, S., Bueno-De-Mesquita, H.B., Peeters, P.H., Lund, E., Weiderpass, E., Redondo, M.L., Sánchez, M.J., Tormo, M.J., Barricarte, A., Larrañaga, N., Ekström, J., Hortlund, M., Lindquist, D., Wareham, N., Travis, R.C., Rinaldi, S., Tommasino, M., Franceschi, S., Riboli, E.: Smoking as a major risk factor for cervical cancer and pre-cancer: results from the EPIC cohort. Int. J. Cancer 135, 453–466 (2014). https://doi.org/10.1002/ijc.28666

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Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia

    Norin Rahayu Shamsuddin

  2. School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Changlun, Malaysia

    Nor Idayu Mahat

Authors
  1. Norin Rahayu Shamsuddin
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  2. Nor Idayu Mahat
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Correspondence to Norin Rahayu Shamsuddin .

Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA (UiTM) Kedah , Sungai Petani, Kedah, Malaysia

    Liew-Kee Kor

  2. Universiti Teknologi MARA (UiTM) Kedah, Sungai Petani, Kedah, Malaysia

    Abd-Razak Ahmad

  3. Universiti Teknologi MARA (UiTM) Kedah, Sungai Petani, Kedah, Malaysia

    Zanariah Idrus

  4. Universiti Teknologi MARA (UiTM) Kedah, Sungai Petani, Kedah, Malaysia

    Kamarul Ariffin Mansor

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Shamsuddin, N.R., Mahat, N.I. (2019). Comparison Between k-Means and k-Medoids for Mixed Variables Clustering. In: Kor, LK., Ahmad, AR., Idrus, Z., Mansor, K. (eds) Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017). Springer, Singapore. https://doi.org/10.1007/978-981-13-7279-7_37

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  • DOI: https://doi.org/10.1007/978-981-13-7279-7_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7278-0

  • Online ISBN: 978-981-13-7279-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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