Introducing WIREs Data Mining and Knowledge Discovery


  • Witold Pedrycz

    1. Department of Electrical and Computer Engineering University of Alberta Edmonton, AB, Canada, and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
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Knowledge discovery and data mining are the landmarks of the information age. Acquiring, storing, and understanding data have posed great challenges and brought a lot of promises. Knowledge discovery in databases (KDD) and data mining (DM) have emerged as high profile, rapidly evolving, badly needed, conceptually advanced, and practically important areas.

In a nutshell, KDD and DM have to deal with a truly remarkable diversity of data and a panoply of applications. To address the needs arising there, we have to engage a number of different methodologies and algorithmic frameworks so that after proceeding with a series of processing steps we end up with well-articulated, easily interpretable, and novel patterns of knowledge buried in the huge heaps of collected data. It is no surprise at all that KDD and DM build upon and creatively exploit well-established principles and methods of statistics, data structures, databases, machine learning, and pattern recognition while bringing to the picture more recent paradigms of computational intelligence with its concepts of granular computing (fuzzy sets, rough sets, and interval analysis), neurocomputing, and evolutionary and population-based optimization. The KDD area is remarkably multifaceted: along with methodological and algorithmic issues one has to raise and carefully address societal questions related to the evident problems of security, privacy, and protection of intellectual property. In essence, the methodology and practice of KDD and DM builds upon interdisciplinarity. Their successes come as a result of vivid cross-disciplinary collaborative efforts taking place among many disciplines of science and engineering.

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, part of the innovative WIREs series of publications from Wiley-Blackwell, 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 area. The key objective is to authoritatively cover the vast territory of DM and KDD. For efficient navigation, several main topic categories have been identified: (1) fundamental concepts of data and knowledge; (2) technologies (including classification, prediction, structure discovery and clustering, machine learning, computational intelligence, statistical fundamentals, and computer architectures for DM); (3) algorithmic developments; (4) application areas (including education and learning, finance and economics, health care, and industry); and (5) commercial, legal, and ethical issues of DM and KDD. The taxonomy is not cast in stone, and the project will evolve to fully reflect the trends of this rapidly progressing area.

There are four types of articles, each offering a different perspective on the subject matter. Overviews provide a broad and relatively nontechnical treatment of important topics at a level suitable for advanced students and for researchers without a strong background in the area. Advanced Reviews, aimed at researchers and students with a strong background in the subject, concentrate on reviewing key areas of research in a citation-rich format similar to that of leading review journals. Focus articles, which are technical in nature, are short papers that describe specific real-world issues, examples, and detailed implementations. Finally, Opinions create a valuable forum for thought-leaders to provide a more individual and personal perspective on the field of DM.

The publisher, Wiley-Blackwell, has put forward this truly innovative and exciting initiative and has given its full backing to the project by providing all necessary support, and by assigning to the WIREs its knowledgeable and enthusiastic staff.

I would like to take this opportunity to express my sincere thanks to the Associate Editors for their dedication and professionalism. I hope that you, the reader, will find this publication of genuine interest and that it will help in your research, educational, and practical endeavors.