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Computer Science - Database Management & Information Retrieval | Data Mining and Knowledge Discovery - incl. option to publish open access (Editorial Board)

Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery

Editor-in-Chief: Johannes Fürnkranz

ISSN: 1384-5810 (print version)
ISSN: 1573-756X (electronic version)

Journal no. 10618

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Editor in Chief
Johannes Fürnkranz,
TU Darmstadt, Germany

Advisory Board
Rakesh Agrawal,
Microsoft Search Labs, USA
Christos Faloutsos, Carnegie Mellon University, USA
Usama Fayyad, Yahoo, Inc., USA
Heikki Mannila, Helsinki University of Technology, Finland
Raghu Ramakrishnan, Yahoo! Research, USA
Padhraic Smyth, University of California, Irvine, USA
Geoff Webb, Monash University, Australia

Action Editors
Charu Aggarwal,
IBM T.J. Watson Research Center, USA
Pierre Baldi, University of California, Irvine, USA
Albert Bifet, LTCI, Télécom ParisTech, Université Paris-Saclay, France
Hendrik Blockeel, Katholieke Universiteit Leuven, Belgium
Toon Calders, Université Libre de Bruxelles, Belgium
Ian Davidson, University of California at Davis, USA
Tina Eliassi-Rad, Lawrence Livermore National Laboratory, USA
Aristides Gionis,
Aalto University, Finland
Bart Goethals, University of Antwerp, Belgium
George Karypis, University of Minnesota, USA
Eamonn Keogh, University of California, Riverside, USA
Kristian Kersting, TU Dortmund, Germany
Donato Malerba, Università degli Studi di Bari Aldo Moro, Italy
Pauli Miettinen, Max-Planck Institute for Informatics, Germany
Panagiotis Papapetrou, Stockholm University, Sweden
Srinivasan Parthasarathy, Ohio State University, USA
Myra Spiliopoulou, Otto-von-Guericke University Magdeburg, Germany
Hanghang Tong, Arizona State University, USA
Jieping Ye,
University of Michigan, Ann Arbor, USA
Fei Wang, Cornell University, USA
Mohammed J. Zaki, Rensselaer Polytechnic Institute, USA

Editorial Board
Anthony Bagnall, University of East Anglia, UK
Roberto Bayardo, Google, Inc., USA
Bettina Berendt, KU Leuven, Belgium 
Michael R. Berthold, University of Konstanz and KNIME, Germany
Albert Bifet, LTCI, Télécom ParisTech, Université Paris-Saclay, France
Francesco Bonchi, Yahoo Labs, Barcelona, Spain
Henrik Boström, Stockholm University, Sweden
Wray Buntine, National ITC Australia, Australia
Andre Ponce de Leon F. de Carvalho, University Sao Paolo, Brazil
Michelangelo Ceci, Università degli Studi di Bari Aldo Moro, Italy
Aniket Chakrabarti, Microsoft, USA
Sanjay Chawla, University of Sydney, Australia
Jesse Davis, Katholieke Universiteit Leuven, Belgium
Tijl De Bie, University of Bristol, UK
Luc De Raedt, Katholieke Universiteit Leuven, Belgium
Charlotte Domeniconi, George Mason University, USA
Saso Dzeroski, Jozef Stefan Institute, Slovenia
Tom Fawcett, Silicon Valley Data Science LLC, USA
Stefano Ferilli, Università degli Studi di Bari Aldo Moro, Italy
Peter Flach, University of Bristol, UK
Eibe Frank, University of Waikato, Hamilton, New Zealand
João Gama, University of Porto, Portugal
Christophe Giraud-Carrier, Brigham Young University, USA
Jingrui He, Arizona State University, USA
Eyke Hüllermeier, University of Paderborn, Germany
Szymon Jaroszewicz, National Institute of Telecommunications, Poland
Shuiwang Ji, Washington State University, USA
Arno Knobbe, LIACS, Universiteit Leiden, Netherlands
Dragi Kocev, Jožef Stefan Institute, Ljubljana, Slovenia
Danai Koutra, University of Michigan, Ann Arbor, USA
Vipin Kumar, University of Minnesota, USA
Mark Last, Ben-Gurion University of the Negev, Israel
Nada Lavrac, Josef Stefan Institute, Slovenia
Zhenhui (Jessie) Li, The Pennsylvania State University, USA
Jefrey Lijffijt, Ghent University, Belgium
Bing Liu, University of Illinois, Chicago, USA
Sofus Macskassy, Facebook, USA
Donato Malerba, Università degli Studi di Bari Aldo Moro, Italy
Wagner Meira, Universidade Federal de Minas Gerais, Brazil
Prem Melville, Social Alpha, USA
Ernestina Menasalva, Universidad Politécnia De Madrid, Spain
Dunja Mladenic, Jozef Stefan Institute, Slovenia
Katharina Morik, Technische Universität Dortmund, Germany
Abdullah Mueen, University of New Mexico, USA
Sriraam Natarajan, University Indiana, Bloomington, USA
Siegfried Nijssen, LIACS, Universiteit Leiden, Netherlands
Evangelos E. Papalexakis, University of California Riverside, USA
Panagiotis Papapetrou, Stockholm University, Sweden 
Mykola Pechenizkiy, Eindhoven University of Technology, Netherlands
Ruggero G. Pensa, University of Torino, Italy
Francois Petitjean, Monash University, Australia
Bernhard Pfahringer, University of Waikato, Hamilton, New Zealand
B. Aditya Prakash, Virginia Tech, USA
Foster Provost, New York University, USA
Chedy Raïssi, INRIA Paris, France
Naren Ramakrishnan, Virginia Tech, USA
Jan Ramon, INRIA Lille Nord Europe, France
Tobias Scheffer, University of Potsdam, Germany
Arno Siebes, Universiteit Utrecht, Netherlands
Jie Tang, Tsinghua University, China
Nikolaj Tatti, Helsinki Institute of Information Technology, Aalto University, Finland
Hannu Toivonen, University of Helsinki, Finland
Vincent S. Tseng, National Chiao Tung University, Taiwan
Grigorios Tsoumakas, University Thessaloniki, Greece
Antti Ukkonen, University of Helsinki, Finland
Matthijs van Leeuwen, Leiden University, Netherlands
Jilles Vreeken, Max Planck Institute for Informatics and Saarland University, Germany
Tim Weninger, University of Notre Dame, USA
Albrecht Zimmermann, INSA Lyon, France
Indrė Žliobaitė, University of Helsinki, Finland

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  • Aims and Scope

    Aims and Scope


    Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.

    KDD is concerned with issues of scalability, the multi-step knowledge discovery process for extracting useful patterns and models from raw data stores (including data cleaning and noise modelling), and issues of making discovered patterns understandable.

    Data Mining and Knowledge Discovery is the premier technical publication in the field, providing a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities. The journal publishes original technical papers in both the research and practice of DMKD, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications. Short (2-4 pages) application summaries are published in a special section.

    The journal accepts paper submissions of any work relevant to DMKD. A summary of the scope of Data Mining and Knowledge Discovery includes:

    Theory and Foundational Issues: Data and knowledge representation; modelling of structured, textual, and multimedia data; uncertainty management; metrics of interestingness and utility of discovered knowledge; algorithmic complexity, efficiency, and scalability issues in data mining; statistics over massive data sets.

    Data Mining Methods: including classification, clustering, probabilistic modelling, prediction and estimation, dependency analysis, search, and optimization.

    Algorithms for data mining including spatial, textual, and multimedia data (e.g., the Web), scalability to large databases, parallel and distributed data mining techniques, and automated discovery agents.

    Knowledge Discovery Process: Data pre-processing for data mining, including data cleaning, selection, efficient sampling, and data reduction methods; evaluating, consolidating, and explaining discovered knowledge; data and knowledge visualization; interactive data exploration and discovery.

    Application Issues: Application case studies; data mining systems and tools; details of successes and failures of KDD; resource/knowledge discovery on the Web; privacy and security issues.
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