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  DMIN'09 Programme
 DMIN'09 Tutorials
 DMIN'08 Special Sess.

Tutorial Sessions

All tutorials are free to registered conference attendees of all conferences held at WOLDCOMP'09. Those who are interested in attending one or more of the tutorials are to sign up on site at the conference registration desk in Las Vegas. A complete & current list of WORLDCOMP Tutorials can be found here.

In addition to tutorials at other conferences, DMIN'09 provides a set of tutorials dedicated to Data Mining topics. The 2007 key tutorial was given by Prof. Eamonn Keogh on Time Series Clustering. The 2008 key tutorial was presented by Mikhail Golovnya (Senior Scientist, Salford Systems, USA) on Advanced Data Mining Methodologies.

This year DMIN will provide the following tutorials:

Tutorial A
Organizer: Nitesh V. Chawla, University of Notre Dame, USA


Topic: Data Mining with Sensitivity to Rare Events and Class Imbalance
Webpage http://www.cse.nd.edu/~nchawla/
Date & Time July 13, 2009 (6:00pm - 9:00pm)
Location Gold Room
Description Recent years brought increased interest in applying data mining techniques to difficult 'real-world' problems, many of which are characterized by imbalanced learning data, where at least one class is much rarer relative to others. Examples include (but are not limited to): fraud/intrusion detection, risk management, medical diagnosis/monitoring, bioinformatics, text categorization and personalization of information. The problem of imbalanced data is also often associated with asymmetric costs of misclassifying elements of different classes. Additionally the distribution of the test data may differ from that of the learning sample and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, will not be appropriate when the data is imbalanced and/or the costs of different errors vary markedly.

This tutorial will introduce the problem of class imbalance, address the scope of solutions available, present and contrast the appropriate metrics for evaluating performance, and discuss the applications with case studies.
Short Bio Nitesh Chawla is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. He directs the Data Inference Analysis and Learning Lab (DIAL) and co-directs the Interdisciplinary Center of the Network Science and Applications (iCenSA) at Notre Dame. His research is primarily focused on machine learning, data mining, and social and dynamic networks. His work has led to applications in various domains including biology, medicine, finance, security, social science, fraud detection, intrusion detection, and text categorization. He is on the editorial board of IEEE Transactions on Systems, Man and Cybernetics Part B. He has received various awards and acknowledgements. He received the NAE FIE New Faculty Fellowship in 2005. His current research is supported form NSF, DOD, NWICG, NIJ, and industry sponsors.


Tutorial B
Organizer: Peter Geczy, National Institute of Advanced Industrial Science and Technology (AIST), Japan


Topic: Emerging Human-Web Interaction Research
Date & Time July 14, 2009 (6:00 – 8:00pm)
Location Ballroom 1
Description Abstract:
World wide web has evolved from its earlier static form to an interactive multimedia environment. Richness of interactions is rapidly approaching that of the conventional stand-alone applications. Human interactivity with web-based environments has been gaining increasing importance in both web research and e-commerce. Mining and exploring human-web interactions bring numerous challenges as well as opportunities. We will probe into the processes and methods of human-web interaction research ranging from data acquisition techniques, throughout analytics, to applications. Accounting for the latest advances in the field, we will project the prospective future trends.

The primary objective of the tutorial is to provide clear, yet reasonably comprehensive, overview of the underlying principles, current approaches, and potential future trends. Knowledge of the state-of-the-art in human-web interaction research should be beneficial to a wide spectrum of individuals studying, utilizing, designing, and/or managing web-based information systems.

The tutorial aims to approach a broad audience including, but not limited to:
- Students and Educators
- Academics and Researchers
- Practitioners and Managers

The topic shall be presented in an accessible and intuitive manner without extensive technical details.

The material/slides will be provided after the conference as pdf-file on this website.

Short Bio Dr. Peter Geczy is a senior scientist at The National Institute of Advanced Industrial Science and Technology (AIST). He also held positions at The Institute of Physical and Chemical Research (RIKEN) and The Research Center for Future Technology. His interdisciplinary scientific interests encompass domains of human interactions and behavior in digital environments, information systems, knowledge management and engineering, data and web mining, artificial intelligence, and machine learning. His recent research focus also extends to the spheres of service science, engineering, management, and computing. He received several awards in recognition of his accomplishments. Dr. Geczy has been serving on various professional committees, editorial boards, and has been a distinguished speaker in academia and industry.


Tutorial C
Organizer: Asim Roy, Arizona State University


Topic: Autonomous Machine Learning
Date & Time July 15, 2009 (6:00pm - 8:00pm)
Location Ballroom 1

Autonomous machine learning has become a top priority in science and engineering of learning. In July 2007, NSF had a workshop on the “Future Challenges for the Science and Engineering of Learning.” Here is the summary of the “Open Questions in Both Biological and Machine Learning” from the workshop (http://www.cnl.salk.edu/Media/NSFWorkshopReport.v4.pdf).

“Biological learners have the ability to learn autonomously, in an ever changing and uncertain world. This property includes the ability to generate their own supervision, select the most informative training samples, produce their own loss function, and evaluate their own performance. More importantly, it appears that biological learners can effectively produce appropriate internal representations for composable percepts - a kind of organizational scaffold - as part of the learning process. By contrast, virtually all current approaches to machine learning typically require a human supervisor to design the learning architecture, select the training examples, design the form of the representation of the training examples, choose the learning algorithm, set the learning parameters, decide when to stop learning, and choose the way in which the performance of the learning algorithm is evaluated. This strong dependence on human supervision is greatly retarding the development and ubiquitous deployment of autonomous artificial learning systems. Although we are beginning to understand some of the learning systems used by brains, many aspects of autonomous learning have not yet been identified.”

This dismal NSF characterization of the state of our learning systems opens the door to creating a new generation of learning algorithms. And conferences such as DMIN could become the focal point for research collaboration on this new breed of learning algorithms.

The objective of this tutorial is to present some new ideas regarding brain-like learning, ideas that can lead to the development of autonomous learning methods. Autonomous learning is extremely important for robotics. For autonomous robots that can learn on their own like humans, we have to have tweak-free learning algorithms that can design and train computational structures (e.g. neural networks) on their own without any kind of human intervention.

Structure of the tutorial:

  • Provide an overview of a broad set of principles for designing and constructing autonomous learning algorithms. Present some new ideas about brain-like learning that differ from current connectionist approaches.

  • Discuss one particular autonomous learning algorithm for pattern classification problems. Give a demonstration of this autonomous learning algorithm. Summarize its basic features and design principles.

  • As noted in the NSF report, autonomous learning is the technology we need and it is important that we get organized and focus on this new breed of learning algorithms. So there will be some open discussion on this issue. We could take this opportunity to form a research group within DMIN for collaboration on autonomous learning systems.

Short Bio

Asim Roy is a Professor of Information Systems at Arizona State University. He received his M.S. in Operations Research from Case Western Reserve University, Cleveland, Ohio, and Ph.D. in Operations Research from University of Texas at Austin. He has been a Visiting Scholar at Stanford University, visiting the PDP group of David Rumelhart in the Psychology department in the early 90s. He was the Letters Editor of IEEE Transactions on Neural Networks and has served on the organizing committees of many scientific conferences.

Asim’s research interests are in neural networks, automated machine learning and data mining, pattern recognition, prediction and forecasting, intelligent systems, information retrieval (search) and nonlinear multiple objective optimization. His research has been published in Management Science, Decision Analysis, The ORSA Journal on Computing, Naval Research Logistics, IEEE Transactions on Neural Networks, IEEE Transactions on Fuzzy System, Neural Networks, Neural Computation and other journals.

Asim has recently published a new theory for brain-like learning and computing. This new theory challenges the classical ideas that have dominated the field of brain-like computing for the last 50 years. PhsyOrg.com recently wrote a story on this new brain theory (http://www.physorg.com/news146319784.html). He has been invited for plenary talks and for tutorials, workshops and short courses on his new learning theory and methods at many national and international conferences.


Tutorial D
Organizer: Dan Steinberg, CEO of Salford Systems


Topic: A Tour of Advanced Data Mining Methodologies
Date & Time July 15, 2009 (6:00pm - 9:00/9:30pm)
Location Ballroom 4
Description Abstract:

Dr. Dan Steinberg, President and CEO of Salford Systems, will discuss the classic CART (classification and regression trees) technique, as well as advanced data mining techniques recently developed by Stanford University Professor Jerome Friedman and University of California Professor Emeritus Leo Breiman. Methodologies and real-world applications will be presented for the following:

  • CART, the classic decision tree
  • MARS (multivariate adaptive regression splines), a flexible, highly automated regression technique
  • TreeNet and RandomForests, which leverage the predictive power of CART models by combining a large number of trees together using either boosting or bootstrap aggregation approaches.


To provide an introduction to and overview of Data Mining Analysis and to provide practical examples to assist attendees in conducting their own analyses.

Intended audience:

  • Instructors wishing to learn more about data mining so they can include some coverage in their classes;
  • Applied Statisticians wanting to learn new tools for exploratory and non-parametric data analysis; and,
  • Researchers who have previously worked with data mining and have been mystified by earlier versions of the documentation and output.
Short Bio

Dan Steinberg, the President and CEO of Salford Systems, founded the company in 1983 just after receiving his Ph.D. in Economics at Harvard.

He has also served as Assistant Professor of Economics at the University of California, San Diego, and participated in dozens of consulting projects for Fortune 100 clients. Dr. Steinberg has published articles in statistics, econometrics, computer science, and marketing journals, and has been a featured data mining issues speaker for the American Marketing Association, the American Statistical Association, the Direct Marketing Association and the Casualty Actuarial Society.



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Robert Stahlbock
General Conference Chair

E-mail: conference-chair@dmin--2009.com

Sven F. Crone

Programme Chair

E-mail: programme-chair@dmin--2009.com


Philippe Lenca

Tutorial Chair

E-mail: tutorial-chair@dmin--2009.com


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