MAT-63806 Introduction to Data Mining, 4 cr, 1/2015-16


Lecturer

Professor Esko Turunen, PhD
Office: TD 421
Office hours: Wednesday 12.15-13.00
e-mail: esko.turunen@tut.fi


Schedule

Lectures will start on Wednesday, the 26.th of August at 9:15 in lecture room TC315 and will last the teaching period I (30 hours in all). Lectures, demonstrations as well as computer demonstrations will be held in lecture room TC315 on Wednesdays and Thursdays and at 9:15 - 12:00. There are no special hours for demonstrations: exercises and computer tasks will be done during lectures. Each student should write and submit his learning notes – detailed instruction will be given during the course.

Content: the GUHA method in Data Mining

Knowledge discovery in databases (KDD) is defined to be a non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. In practise, we have a huge data matrix and we are interested in finding some hidden information in this data. The GUHA method is one of the oldest data mining methods and is based on a special extension of classical logic. We study the mathematical foundations of the GUHA method and Lisp Miner - a computer implementation of GUHA - and see several real world applications. The skeleton of this recently updated part can be down loaded here, however, this is just a skeleton that will be completed during the course.
Part 1

Part 2

Part 3

Part 4

Part 5

Part 6

Part 7

Part 8

Part 9

Part 10

You might also like to have a look at the earlier implementations
of a related data mining course, see
Data mining - an overview

Introduction to the GUHA method

GUHA - theory 1

GUHA - theory 2

GUHA - theory 3

GUHA - theory 4

GUHA - applications

Literature:
Rauch, J. : Observational Calculi and Association Rules. Springer-Verlag, 2013
Rauch, J ; LISpMiner (http://lispminer.vse.cz/


Last updated 11.08.2015.