Editorial for issue 3/2017

Editorial for issue 3/2017 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Data Analysis and Classification Springer Journals

Editorial for issue 3/2017

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Statistics; Statistical Theory and Methods; Data Mining and Knowledge Discovery
ISSN
1862-5347
eISSN
1862-5355
D.O.I.
10.1007/s11634-017-0291-0
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Advances in Data Analysis and ClassificationSpringer Journals

Published: Aug 19, 2017

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