Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

WIREs Data Mining and Knowledge Discovery has been accepted by Thomson Reuters. Check out our top-cited papers below:

Classification and regression trees
Wei-Yin Loh

Robust statistics for outlier detection
Peter J. Rousseeuw, Mia Hubert

Density-based clustering
Hans-Peter Kriegel1, Peer Kröger, Jörg Sander, Arthur Zimek

Multivariate image mining
Julia Herold, Christian Loyek, Tim W. Nattkemper

WIREs Data Mining and Knowledge Discovery, part of the innovative WIREs series of publications, comes as a unique and timely publishing undertaking that caters to a broad audience of researchers and practitioners. It is designed to appeal both to experts and to newcomers to the field, as well as to senior undergraduate and graduate students and all those who wish to be kept fully informed about the most recent developments in the areas of Knowledge Discovery in Databases (KDD) and Data Mining (DM).

To read a complete overview please click here.

WIREs Data Mining and Knowledge Discovery has been accepted by Thomson Reuters. The journal will be covered in the Science Citation Index Expanded and Current Contents: Engineering, Computing & Technology.

Volume 1, Issue 2 of WIREs Data Mining and Knowledge Discovery is available online as a free sample issue. Click the below image to see the issue's full table of contents.

Articles in this issue include:

Mining proteomic data for biomedical research
Laura L. Elo, Benno Schwikowski

Knowledge discovery in data streams with regression tree methods
Dima Alberg, Mark Last, Abraham Kandel

Protecting Sensitive knowledge in association patterns mining
Guanling Lee, Yi Chun Chen

Your institute may have registered for complimentary access to  WIREs Data Mining and Knowledge Discovery. From the 2013 Volume your institute will need a subscription. Make sure your library knows you want to retain access to this title.

Recommend to your Librarian