Tutorial Speaker: Dr. Andrew Kusiak

Dr. Andrew Kusiak is a Professor in the Department of Mechanical and Industrial Engineering at the University of Iowa in Iowa City , Iowa . He is interested in applications of computational intelligence in automation, manufacturing, product development, pharmaceutical industry, and healthcare. Dr. Kusiak has published numerous books and technical papers in journals sponsored by professional societies, such as AAAI, ASME, IEEE, IIE, ESOR, IFIP, IFAC, INFORMS, ISPE, and SME. He speaks frequently at international meetings, conducts professional seminars, and consults for industrial corporations. Dr. Kusiak serves on editorial boards of numerous journals, edits book series, and is the Editor-in-Chief of the Journal of Intelligent Manufacturing .
Dr. Kusiak's recent publications can be viewed at http://www.icaen.uiowa.edu/%7Eankusiak/recent-paper.html
For additional info see http://css.engineering.uiowa.edu/~ankusiak/

Tutorial Topic: Data Mining and Farming in Supply Chain Management

Many data mining projects are based on data sets collected for various purposes, ranging from routinely collected data to process improvement projects and data required by regulatory bodies. In some cases, the set of collected features might be large (a wide data set) and more than sufficient for extraction of knowledge. In other cases, the data set might be narrow and insufficient to extract meaningful rules or the data may even not exit.

The mining of wide data sets has received much attention in data mining literature. Feature selection models and algorithms have been developed for such data sets.

Determining features for which data should be collected in the absence of a data set or its partial availability (a narrow set) has not been sufficiently addressed in the literature. Yet, this issue is of paramount importance as the interest in data mining is growing. The processes and methods used to determine the most appropriate features for data collection and subsequent data analysis are referred to as data farming. Various data mining and data farming methods will be presented and illustrated with examples.

Tutorial Contents :

1‧  Introduction
2‧ Data mining algorithms
3‧ Feature selection
4‧ Data farming methods and algorithms
5‧  Case studies
6‧ Computer tools