Special Issue "Selected Papers from Text Mining Workshop at the 2012 SIAM International Conference on Data Mining"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 June 2012)
Prof. Dr. Michael W. Berry (Website)
Department of Electrical Engineering and Computer Science, The University of Tennessee, Min H. Kao Building, Suite 401, 1520 Middle Drive, Knoxville, TN 37996, USA
Fax: +1 865 974 4404
Interests: information retrieval, data and text mining, computational science, bioinformatics, and parallel computing
Dr. Jacob Kogan (Website)
Department of Mathematics and Statistics, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland 21250, USA
Fax: +1 410 455 1066
Interests: optimal control theory; finite dimensional optimization; robust stability of control systems; computational information retrieval
The proliferation of digital computing devices and their use in communication continues to result in an increased demand for systems and algorithms capable of mining textual data. Thus, the development of techniques for mining unstructured, semi-structured, and fully structured textual data has become quite important in both academia and industry. As a result, a one-day workshop on text mining was held on April 28, 2012 in conjunction with the SIAM Twelfth International Conference on Data Mining to bring together researchers from a variety of disciplines to present their current approaches and results in text mining. The workshop surveyed the emerging field of text mining-the application of techniques of machine learning in conjunction with natural language processing, information extraction and algebraic/mathematical approaches to computational information retrieval. Many issues are being addressed in this field ranging from the development of new document classification and clustering models to novel approaches for topic detection, tracking, and visualization.
Prof. Dr. Michael W. Berry
Dr. Jacob Kogan
- document ranking and representation
- document classification and clustering
- text summarization and anomaly detection