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Data Mining and Knowledge Discovery

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Welcome to the home page of the newly-founded journal, Data Mining and Knowledge Discovery. The first issue was published in April 1997. Data Mining and Knowledge Discovery is a peer reviewed journal publishing articles on all aspects of Knowledge Discovery in Databases (KDD) and data mining methods for extracting high-level representations (patterns and models) from data. Submissions of high-quality original research or technical survey articles of related fields and techniques are welcome. We also publish application papers as well as short (2-page) application summary articles. The first issue provides examples of accepted articles. See the call for papers for more details and requirements.

The growing importance and success of Data Mining and Knowledge Discovery in Databases (KDD), has led us to form a new technical journal to document research advances by this rapidly growing community. The second International Conference on Knowledge Discovery and Data Mining (KDD-96) held in Portland, OR, USA, attracted over 500 attendees and continued on the success of the first conference, KDD-95. The third conference, KDD-97 is set to take place in August 1997 and will be located near the ASA-97 conference. Special journal and magazine issues as well as books on data mining and KDD have begun to appear with a significant frequency and across a wide ranging spectrum of communities. It is hoped that this newly-founded journal will provide a unified forum for the publication of papers on the theory and practice of KDD, and to serve as a reference for both researchers and application developers from different fields focusing on unifying themes.

KDD Draws on techniques and theories from a multitude of fields, including statistics, pattern recognition, learning, databases, OLAP, optimization, uncertainty modeling, visualization, and high-performance and parallel computing. We aim to make this journal into a unified place where relevant works from all related fields are presented.


  • Usama Fayyad, Microsoft Research, Redmond, WA, USA
  • Heikki Mannila, University of Helsinki, Helsinki, Finland
  • Gregory Piatetsky-Shapiro, Knowledge Stream, Cambridge, MA, USA


See Also