Advances in Data Analysis and Classification Theory, Methods, and Applications in Data Science - Vichi, Maurizio; Gaul, Wolfgang; Okada, Akinori; Weihs, Claus; SpringerLink (Online service) (Berlin/Heidelberg : Springer Berlin Heidelberg : Imprint: Springer.)
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 whatever types of data. It publishes articles on topics as, e.g., 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 whatever type of data, and Applications of advanced methods in specific domains of practice. In particular, this comprises the consideration and handling of new data types as well as the analysis of complex structures such as text data and webfiles. Whereas the discussion of theoretical, statistical, or algorithmic advances in methodology is a major issue (e.g., in classification and clustering), the journal encourages strongly the publication of applications that illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. In addition to contributed papers on specific topics, the journal also publishes survey papers that outline, and illuminate, the basic ideas and techniques of special approaches. On occasion, specialized topics will be presented in a special issue. The journal is supported by several scientific societies which aim to foster the area of classification and data analysis. Supported by the International Federation of Classification Societies Funded by the Italian, German, and Japanese Classification Societies (CLADAG, GfKl, JCS) Officially cited as: Adv Data Anal Classif.
Keywords: Data mining; Statistics; Statistical Theory and Methods; Statistics for Business, Management, Economics, Finance, Insurance; Statistics for Life Sciences, Medicine, Health Sciences; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Statistics for Social Sciences, Humanities, Law; Data Mining and Knowledge Discovery