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Advances in Data Analysis and Classification

Theory, Methods, and Applications in Data Science

Advances in Data Analysis and Classification OnlineFirst articles

19.03.2019 | Regular Article Open Access

Robust and sparse k-means clustering for high-dimensional data

In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is a need for a clustering method which is capable of revealing the …

02.03.2019 | Regular Article

Enhancing techniques for learning decision trees from imbalanced data

Several machine learning techniques assume that the number of objects in considered classes is approximately similar. Nevertheless, in real-world applications, the class of interest to be studied is generally scarce. The data imbalance status may …

21.02.2019 | Regular Article Open Access

Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership

A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal …

15.02.2019 | Regular Article Open Access

Exploration of the variability of variable selection based on distances between bootstrap sample results

It is well known that variable selection in multiple regression can be unstable and that the model uncertainty can be considerable. The model uncertainty can be quantified and explored by bootstrap resampling, see Sauerbrei et al. (Biom J …

12.02.2019 | Regular Article

Discriminant analysis for discrete variables derived from a tree-structured graphical model

The purpose of this paper is to illustrate the potential use of discriminant analysis for discrete variables whose dependence structure is assumed to follow, or can be approximated by, a tree-structured graphical model. This is done by comparing …

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The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.

Supported by the International Federation of Classification Societies, and funded by the Italian, German, and Japanese Classification Societies (CLADAG, GfKl, JCS).

Officially cited as: Adv Data Anal Classif

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