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Advances in Data Analysis and Classification

, Volume 13, Issue 4, pp 819–823 | Cite as

Editorial for issue 4/2019

Editorial

This issue 4 of volume 13 (2019) of the journal Advances in Data Analysis and Classification (ADAC) contains ten articles that deal with bi-clustering, discriminant analysis, supervised learning, robust clustering, variable selection, classification trees, convex clustering, finite mixtures, model based clustering, and a Kendall correlation coefficient for functional data.

In the first paper with the title “Orthogonal nonnegative matrix tri-factorization (ONMTF) based on Tweedie distributions” the authors Hiroyasu Abe and Hiroshi Yadohisapropose a new quite general bi-clustering method for a given nonnegative data matrix that can be used, e.g., for document-term clustering, collaborative filtering etc. While classical ONMTF approaches assume that the error distribution is normal (leading to a least squares approximation problem), which is somewhat inconsistent with nonnegative data entries, the new approach is formulated for the often more adequate class of Tweedie distributions and...

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

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