Clustering Big Data by Extreme Kurtosis Projections
Daniel Peña, Francisco J. Prieto, Janeth Carolina Rendon Aguirre · e-Archivo (Carlos III University of Madrid) · 2017
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.