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

• Presents research and applications on the extraction of knowable aspects from many types of data
• Topics include 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
• Shows how new domain-specific knowledge can be made available from data by skillful use of data analysis methods