Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Cover image for Vol. 6 Issue 4

Edited By: Witold Pedrycz

Impact Factor: 1.759

ISI Journal Citation Reports © Ranking: 2015: 21/105 (Computer Science Theory & Methods); 48/130 (Computer Science Artificial Intelligence)

Online ISSN: 1942-4795

Overview



Aims and Scope


The objectives of WIREs DMKD are to (a) present the current state of the art of data mining and knowledge discovery through an ongoing series of reviews written by leading researchers, (b) capture the crucial interdisciplinary flavor of the field by including articles that address the key topics from the differing perspectives of data mining and knowledge discovery, including a variety of application areas in technology, business, healthcare, education, government and society and culture, (c) capture the rapid development of data mining and knowledge discovery through a systematic program of content updates, and (d) encourage active participation in this field by presenting its achievements and challenges in an accessible way to a broad audience. The content of WIREs DMKD will be useful to upper-level undergraduate and postgraduate students, to teaching and research professors in academic programs, and to scientists and research managers in industry.

The techniques of data mining and knowledge discovery (DMKD) are now being applied in many areas of business and government, such as banking and finance, market research, risk analysis, and counterterrorism. In the sciences, DMKD has become pervasive in such fields as bioinformatics, medical diagnosis, epidemiology, drug discovery, environmental modeling, and meteorological data analysis.


Abstracting and Indexing Information

  • Current Contents: Engineering, Computing & Technology (Thomson Reuters)
  • Science Citation Index Expanded (Thomson Reuters)
  • SCOPUS (Elsevier)
  • The DBLP Computer Science Bibliography (University of Trier)
  • Web of Science (Thomson Reuters)

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