Advances in Data Analysis and Classification
Theory, Methods, and Applications in Data Science
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
Asymptotic comparison of semi-supervised and supervised linear discriminant functions for heteroscedastic normal populations
Kenichi Hayashi (July 2016)
- Journal Title
- Advances in Data Analysis and Classification
- Volume 1 / 2007 - Volume 10 / 2016
- Print ISSN
- Online ISSN
- Springer Berlin Heidelberg
- Additional Links
- Statistical Theory and Methods
- Statistics for Business/Economics/Mathematical Finance/Insurance
- Statistics for Life Sciences, Medicine, Health Sciences
- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
- Data Mining and Knowledge Discovery
- Industry Sectors
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