Journal of Classification

The Journal of Classification presents original work in the field of classification, broadly defined. Articles support advances in methodology, while demonstrating compelling substantive applications. Articles advance understanding in many areas of classification including supervised classification, unsupervised classification (clustering), semi-supervised classification, statistical computing, statistical learning, numerical taxonomy, multivariate statistics, and machine learning. The journal also publishes comprehensive review articles; however, these are usually invited.

Although the principal discipline represented is statistics, a wide range of other disciplines are represented including psychology, biology, information retrieval, computer science, anthropology, archeology, astronomy, business, chemistry, computer science, economics, engineering, geography, geology, linguistics, marketing, mathematics, medicine, political science, psychiatry, sociology, and soil science.

Published three times a year, each issue typically comprises three sections: articles, short notes and comments, and software abstracts.

The Editor-in-Chief is Paul McNicholas, Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada.
 

Officially cited as: J Classif

• Offers papers in the field of classification, numerical taxonomy, multidimensional scaling and other ordination techniques, clustering, tree structures and other network models
• Presents articles, short notes and comments, software abstracts, and book reviews
• Coverage extends to statistics, psychology, biology, information retrieval, anthropology, archeology and more