# 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.