Maastricht School on Data Mining

Website https://project.dke.maastrichtuniversity.nl/datamining/ | Edit Freely

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Deadline: August 23, 2013 | Date: August 27, 2013-August 30, 2013

Venue/Country: Maastricht, Netherlands

Updated: 2013-06-26 04:41:49 (GMT+9)

Call For Papers - CFP

** **

** 11-th MAASTRICHT SCHOOL ON DATA MINING, **

** Maastricht University, **

** Maastricht, The Netherlands **

** **

** August 27 - August 30, 2013 **

** **

** https://project.dke.maastrichtuniversity.nl/datamining/ **

** **

** Apologies if you receive multiple copies of this announcement **

** Please forward to anyone who might be interested **

** **

School on Data Mining

An intensive 4-day introduction to methods and applications

Department of Knowledge Engineering, Maastricht University,

Maastricht, The Netherlands

August 27 - August 30, 2013

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

Intended Audience

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.

Registration

To register for the school please send an email to:

smirnovmaastrichtuniversity.nl

kurt.driessensmaastrichtuniversity.nl

(due to summer holidays please send the registration to both e-mails)

In the e-mail please specify:

- Name

- University / Organisation

- Address

- Phone

- E-Mail

Registration Deadline: August 23, 2013

Registration fees

Academic fee 750 Euros

Non-academic fee 1000 Euros

Coffee breaks are included in the price. The local

cafeteria will be available for lunch (not included).

Regular mail address:

Evgueni Smirnov and Kurt Driessens

Department of Knowledge Engineering

Faculty of Humanities and Sciences

Maastricht University

P.O.Box 616 6200 MD Maastricht

The Netherlands

Phone: +31 (0) 43 38 82023

Fax: +31 (0) 43 38 84897

E-mails: smirnovmaastrichtuniversity.nl

kurt.driessensmaastrichtuniversity.nl