School On Data Mining, Maastricht, Oct 22-24

An intensive 3-day introduction to methods and applications
October 22 - 24, 2012

Introduction
Most business organizations collect terabytes of data about business processes and resources. Usually these data provide just "facts and figures", not knowledge that can be used to understand and eventually re-engineer business processes and resources. Scientific community in academia and business have addressed this problem in the last 20 years by developing a new applied field of study known as data mining.

In practice data mining is a process of extracting implicit, previously unknown, and potentially useful knowledge from data. It employs techniques from statistics, artificial intelligence, and computer science. Data mining has been successfully applied for acquiring new knowledge in many domains (like Business, Medicine, Biology, Economics, Military, etc.). As a result most business organizations need urgently data-mining specialists, and this is the point where this school comes to help.

Description

Our school on data mining tries to find a balance between theory and practice.

Each lecture is accompanied by a lab in which participants experiment with the techniques introduced in the lecture. The lab tool is Weka, one of the most advanced data-mining environments. A number of real data sets will be analysed and discussed. In the end of the school participants develop their own ability to apply data-mining techniques for business and research purposes.

Content

The school will cover the topics listed below.

• The Knowledge Discovery Process
• Data Preparation
• Basic Techniques for Data Mining:
  • Decision-Tree Induction
  • Rule Induction
  • Instance-Based Learning
  • Bayesian Learning
  • Support Vector Machines
  • Regression Techniques
  • Clustering Techniques
  • Association Rules
• Tools for Data Mining
• How to Interpret and Evaluate Data-Mining Results

This school is intended for four groups of data-mining beginners: students, scientists, engineers, and experts in specific fields who need to apply data-mining techniques to their scientific research, business management, or other related applications.

Prerequisites
The school does not require any background in databases, statistics, artificial intelligence, or machine learning. A general background in science is sufficient as is a high degree of enthusiasm for new scientific approaches.

Certificate

Upon request a certificate of full participation will be provided after the school.

For registration, fees, and additional information, please visit

www.unimaas.nl/datamining/