** Apologies for cross-postings. Please send to interested colleagues and research groups in this area **
==================================================================== Call for Papers Knowledge and Information Systems (KAIS) Special Issue on Mining Low-Quality Data http://www.cs.uvm.edu/~xqzhu/kais/kais_mld.htm -------------------------------------------------------------------------- -------- Knowledge Discovery and Data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. But real-world data is rarely perfect and can often suffer from corruptions that may impact interpretations of the data, models created from the data, and decisions made on the data. A general consensus among data mining practitioners is that low data quality often leads to wrong decisions or even ruins the projects ("garbage in, garbage out"), if no proper preprocessing techniques have been adopted in advance. As data mining is increasingly recognized as a key technology to analyzing and understanding the data, the need for knowledge discovery from real-world low-quality data becomes not just overwhelming, but compelling. As a result, data quality related issues have become more and more crucial and have consumed a majority of the time and budget of data mining. While mining low-quality data requires systematic efforts on data quality management, data preparation and the actual mining procedures, previous research endeavors have been mainly focused on each separate part. With existing efforts, the data mining procedures are often isolated from the data quality management and data preprocessing modules, and therefore, have no awareness of the underlying data quality. In addition, since no data preprocessing techniques can result in perfect data, a noise tolerant mining approach is always preferred for many real-world applications, regardless of whether data preparation and data quality management modules have been incorporated. To bridge the gap between data quality management, data preparation and the mining procedures, we need systematic research on how to unify these three components for better performances. This special issue is intended to provide a focal point for research that advances knowledge discovery from low-quality data. We solicit high-quality, original papers, which address issues related to data quality and data mining, including but not limited to the following topics: Data Quality Management + Data and Information Quality Assessment + Data Quality Models and Theoretical Foundations + Domain-Specific Data Quality Evaluation + Metrics for Quality Characterization + Data Quality Monitoring Data Enhancement + Data Cleansing, Noise Detection and Data Correction + Resolving Data Redundancy, Inconsistency, Duplicates, and Conflicts + Data/Feature Reduction, Instance/Feature Ranking + Data Editing and Imputation, Data Acquisition and Filling Incomplete Values + Data Transformations, Reconciliation, and Consolidation + Intelligent Tools for Data Enhancement Data Mining Algorithms with Low-Quality Data + Integrating Data Quality Measures for Effective Mining + Quality Awareness Data Mining + Noise Tolerant Data Mining **************************************************************************** ******* Submission Guidelines We strongly encourage authors to submit their manuscripts in PDF through the journal submission website at http://www.cs.uvm.edu/~kais/ or via email to [EMAIL PROTECTED] Please mention this special issue in your submission remarks. **************************************************************************** Important Dates Manuscript Submission: May 1, 2005 Acceptance Notification: September 1, 2005 Final Manuscript Due: September 30, 2005 Publication: 1st Half, 2006 **************************************************************************** Special Issue Guest Editors Dr. Xingquan Zhu ([EMAIL PROTECTED], University of Vermont) Dr. Ian Davidson ([EMAIL PROTECTED], State University of New York at Albany) Dr. Shichao Zhang ([EMAIL PROTECTED], University of Technology, Sydney) Dr. Taghi M. Khoshgoftaar ([EMAIL PROTECTED], Florida Atlantic University) **************************************************************************** Please refer to the special issue website http://www.cs.uvm.edu/~xqzhu/kais/kais_mld.htm for more details. _______________________________________________
