Date: Tue, 01 Jun 1999 11:30:53 +0900 From: motoda@ar.sanken.osaka-u.ac.jp, Hiroshi Motoda Subject: Second Call for Papers on the special issue on Instance Selection Call for Papers - INSTANCE SELECTION A Special Issue of the Data Mining and Knowledge Discovery Journal http://www.comp.nus.edu.sg/~liuh/dmkd.html Due date: 18 Sept 1999, electronic submission INTRODUCTION Knowledge discovery and data mining (KDD) is growing rapidly as computer technologies advance. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue. Algorithm scale-up is one and data reduction is another. Instance, example, or tuple selection is about algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers other methods that require search. Examples can be found in density estimation - finding the representative instances (data points) for each cluster, and boundary hunting - finding the critical instances to form boundaries to differentiate data points of different classes. Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. (edited GPS) IMPORTANT DATES 18 September, 1999 - Submissions due CONTACT INFORMATION Please direct any enquiries to the guest editors: Huan Liu, liuh@comp.nus.edu.sg, National University of Singapore Hiroshi Motoda, motoda@sanken.osaka-u.ac.jp, Osaka University, Japan. Please submit your work electronically (postscript file) to either guest editor. If you have to submit it in hard copy, please discuss it with the guest editors first. INFORMATION about the JOURNAL Data Mining and Knowledge Discovery, Kluwer Academic Publishers. http://www.wkap.nl/journalhome.htm/1384-5810 Editors-in-Chief: Usama Fayyad, Heikki Mannila.
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