New statistical pattern recognition and modeling techniques that work with high dimensional, large volume data are still needed. On the development and applications fronts plenty of good work needs to be achieved in building up an infra-structure of connectivity to databases, integrated mining tools, and systems to make it easy to develop specialized data mining applications. With the second issue of this journal, we continue to pursue our mission of bringing forth methods and techniques from the variety of fields that constitute the KDD community. The paper by Zhang, Ramakrishnan, and Livny presents a database approach to the fundamental and important problem of clustering with large databases. The papere by Mangasarian shows how mathematical programming techniques from the operations research community can play a role in classification and clustering.
Cooper presents an efficient algorithms for learning graphical probabilistic models from data.
See the article by Heckerman in Issue 1 of this journal for a tutorial on this topic. Finally, we continue our Brief Application Summary section of the journal with the paper by Cox, Eick, Wills, and Brachman. This paper shows an important application to a large analysis problem, with a heavy emphasis on a visualization-based approach to help people detect new and interesting patterns. In conclusion, I continue to be very optimistic about the prospects for Data Mining and KDD and the great impact we stand to bring to the way people interact with large databases. His research interests include knowledge discovery in large databases, data mining, machine learning, statistical pattern recognition, and clustering.