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Zeitschrift

Advances in Data Analysis and Classification

Theory, Methods, and Applications in Data Science

Advances in Data Analysis and Classification OnlineFirst articles

28.05.2020 | Regular Article

The ultrametric correlation matrix for modelling hierarchical latent concepts

Many relevant multidimensional phenomena are defined by nested latent concepts, which can be represented by a tree-structure supposing a hierarchical relationship among manifest variables. The root of the tree is a general concept which includes …

26.05.2020 | Regular Article Open Access

Data generation for composite-based structural equation modeling methods

Examining the efficacy of composite-based structural equation modeling (SEM) features prominently in research. However, studies analyzing the efficacy of corresponding estimators usually rely on factor model data. Thereby, they assess and analyze …

25.05.2020 | Regular Article

Simultaneous dimension reduction and clustering via the NMF-EM algorithm

Mixture models are among the most popular tools for clustering. However, when the dimension and the number of clusters is large, the estimation of the clusters become challenging, as well as their interpretation. Restriction on the parameters can …

25.05.2020 | Regular Article

Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data

Topic detection in short textual data is a challenging task due to its representation as high-dimensional and extremely sparse document-term matrix. In this paper we focus on the problem of classifying textual data on the base of their (unique) …

20.05.2020 | Regular Article

Clustering discrete-valued time series

There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-valued AutoRegressive (INAR) type models. The INAR type models can …

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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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