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Clustering Big Data by Extreme Kurtosis Projections


  • Rendon Aguirre, Janeth Carolina
  • Prieto Fernández, Francisco Javier
  • Peña Sánchez de Rivera, Daniel


Clustering Big Data is an important problem because large samples of many variables are usually heterogeneous and include mixtures of several populations. It often happens that only some of a large set of variables are useful for clustering and working with all of them would be very inefficient and may make more difficult the identification of the clusters. Thus, searching for spaces of lower dimension that include all the relevant information about the clusters seems a sensible way to proceed in these situations. Peña and Prieto (2001) showed that the extreme kurtosis directions of projected data are optimal when the data has been generated by mixtures of two normal distributions. We generalize this result for any number of mixtures and show that the extreme kurtosis directions of the projected data are linear combinations of the optimal discriminant directions if we knew the centers of the components of the mixture. In order to separate the groups we want directions that split the data into two groups, each corresponding to different components of the mixture. We prove that these directions can be found from extreme kurtosis projections. This result suggests a new procedure to deal with many groups, working in a binary decision way and deciding at each step if the data should be split into two groups or we should stop. The decision is based on comparing a single distribution with a mixture of two distribution. The performance of the algorithm is analyzed through a simulation study.

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  • Rendon Aguirre, Janeth Carolina & Prieto Fernández, Francisco Javier & Peña Sánchez de Rivera, Daniel, 2017. "Clustering Big Data by Extreme Kurtosis Projections," DES - Working Papers. Statistics and Econometrics. WS 24522, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:24522

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    1. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.
    2. Chris Fraley & Adrian E. Raftery, 1999. "MCLUST: Software for Model-Based Cluster Analysis," Journal of Classification, Springer;The Classification Society, vol. 16(2), pages 297-306, July.
    3. Fraiman, Ricardo & Justel, Ana & Svarc, Marcela, 2008. "Selection of Variables for Cluster Analysis and Classification Rules," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1294-1303.
    4. Sijian Wang & Ji Zhu, 2008. "Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data," Biometrics, The International Biometric Society, vol. 64(2), pages 440-448, June.
    5. Raftery, Adrian E. & Dean, Nema, 2006. "Variable Selection for Model-Based Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 168-178, March.
    6. Maugis, C. & Celeux, G. & Martin-Magniette, M.-L., 2009. "Variable selection in model-based clustering: A general variable role modeling," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3872-3882, September.
    7. Witten, Daniela M. & Tibshirani, Robert, 2010. "A Framework for Feature Selection in Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 713-726.
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