Adv Data Anal Classif (2017) 11:441–444 DOI 10.1007/s11634-017-0291-0 EDITORIAL © Springer-Verlag GmbH Germany 2017 The present issue 3 of volume 11 (2017) of the journal Advances in Data Analysis and Classiﬁcation (ADAC) includes articles which deal with: robust classiﬁers for multi- variate and functional data, constrained clustering, fuzzy neural clustering network, density based trajectory clustering, ﬂower pollination search algorithm, algorithm to identify the prior probabilities for classiﬁcation problem and general location model. The ﬁrst article on “Multivariate and functional classiﬁcation using depth and distance”, written by Mia Hubert, Peter Rousseeuw and Pieter Segaert, proposes a new non-parametric classiﬁcation algorithm that should be robust to outliers and invariant to linear transformations of the data (either multivariate data vectors or functions). The basic approach proceeds by (1) deﬁning either a distance between data points and classes, or a measure for the outlyingness of data points, (2) considering, for each data point, the vector of distances to the classes (DistSpace transform), and (3) applying the classical k-NN classiﬁer to these transformed data vectors. Apart from this “classiﬁcation in distance space” the major innovation of this paper lies in the choice of the distance and the outlyingness measure in order to attain non-parametrics.
Advances in Data Analysis and Classification – Springer Journals
Published: Aug 19, 2017
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