Data Mining is the automatic discovery of patterns, associations, changes, and anomalies in data sets. Scaling data mining to massive data sets requires new algorithms and combining techniques from high performance computing and data management with techniques from statistics and machine learning. This workshop will emphasize techniques to scale and parallelize algorithms. Of particular interest is the application of methods to specific types of problems and the areas of success and failure. Some techniques of interest are: tree-based statistical methods, graphical models, linear algebra, neural nets, combinatorial methods, meta learning, model selection and model averaging, and applications of data mining to information retrieval.
Call for Participation:
Presentations are solicited on the various mathematical methods and the applications to which they have been applied. There will be a small amount of support available for participants. If you would like to give a talk, please send an abstract to firstname.lastname@example.org.
On line registration is encouraged or send email including your name, institutional affiliation, email address, and the dates you plan to attend to Ms. Sandy Barbu at email@example.com