Download Data Mining - Soft Computing and Intelligent Information Systems

Data Mining and Soft Computing
Francisco Herrera
Research Group on Soft Computing and
Information Intelligent
g
Systems
y
((SCI2S))
Dept. of Computer Science and A.I.
University of Granada, Spain
Email: [email protected]
http://sci2s.ugr.es
http://decsai.ugr.es/~herrera
Data Mining and Soft Computing
In this course:
We will introduce the Data Mining and
K
Knowledge
l d Di
Discovery area: steps,
t
ttask,
k
challenges ..
We will introduce Soft Computing
p
g techniques:
q
Fuzzy Logic, Genetic Algorithms, …
… and we will present the use of Soft Computing
tecniques in Data Mining
2
Data Mining and Soft Computing
Material of this course at:
http://sci2s.ugr.es/docencia/asignatura.php?id_asignatura=14
3
Data Mining and Soft Computing
„
Data mining:
„
Extraction of interesting (non-trivial, implicit,
previously
i
l unknown
k
and
d potentially
t ti ll useful)
f l)
information or patterns from data in large databases
K
Knowledge
l d
Patterns
Target
d t
data
Processed
data
Data Mining
Interpretation
Evaluation
data
Preprocessing
P
i
& cleaning
Selection
4
Data Mining
How can I analyze this data?
Knowledge
We have rich data,
but poor information
Data mining-searching for knowledge
gp
patterns) in yyour data.
(interesting
J. Han, M. Kamber. Data Mining. Concepts and Techniques
Morgan Kaufmann, 2006 (Second Edition)
5
S f C
Soft
Computing
i
Soft computing refers to a collection of computational techniques in
computer science, machine learning and some engineering disciplines,
which study, model, and analyze very complex phenomena: those for which
more conventional
ti
l methods
th d have
h
nott yielded
i ld d llow cost,
t analytic,
l ti and
d
complete solutions.
Prof. Zadeh:
"...in contrast to traditional hard computing,
soft computing exploits the tolerance for
i
imprecision,
ii
uncertainty,
i
andd partial
i l truth
h to
achieve tractability, robustness, low solutionpp with reality”
y
cost,, and better rapport
6
Lotfi A. Zadeh
Introduce
“Fuzzy Logic” in 1965
and “Soft Computing”
in 1992.
C
Computational
t ti l Intelligence
I t lli
The Field of Interest of the Society shall be the theory,
d i
design,
application,
li ti
and
d development
d
l
t off biologically
bi l i ll
and linguistically motivated computational paradigms
emphasizing neural networks,
networks connectionist systems
systems,
genetic algorithms, evolutionary programming, fuzzy
y
, and hybrid
y
intelligent
g
systems
y
in which these
systems,
paradigms are contained.
7
Data Mining and Soft Computing
„
Contents:
P t I.
Part
I Principles
P i i l off Data
D t Mining
Mi i
‰Introduction
‰Introd
ction to Data Mining and Kno
Knowledge
ledge
Discovery
‰Data Preparation
‰Introduction to Prediction, Classification, Clustering
and Association
‰Data Mining - From the Top 10 Algorithms to the New
Challenges
8
Data Mining and Soft Computing
„
Contents:
P t II.
Part
II Soft
S ft Computing
C
ti Techniques
T h i
in
i Data
D t Mining
Mi i
‰Introduction
‰Introd
ction to Soft Computing.
Comp ting Foc
Focusing
sing o
ourr
attention in Fuzzy Logic and Evolutionary
Computation
‰Soft Computing Techniques in Data Mining: Fuzzy
Data Mining and Knowledge Extraction based on
Evolutionary Learning
‰ Genetic Fuzzy Systems: State of the Art and New
Trends
9
Data Mining and Soft Computing
„
Contents:
P t III.
Part
III D
Data
t Mi
Mining:
i
S
Some Ad
Advanced
d Topics
T i
‰Some Advanced
Ad anced Topics I:
I Classification with
ith
Imbalanced Data Sets
‰ Some Advanced Topics II: Subgroup Discovery
‰Some advanced Topics III: Data Complexity
‰Final talk: How must I Do my Experimental Study?
Design of Experiments in Data Mining/
Computational Intelligence. Using Non-parametric
Tests. Some Cases of Study.
10
Bibliography
J. Han,
J
Han M
M. Kamber
Kamber.
Data Mining. Concepts and Techniques
Morgan Kaufmann, 2006 (Second Edition)
http://www.cs.sfu.ca/~han/dmbook
I.H. Witten, E. Frank.
Data Mining: Practical Machine Learning Tools and Techniques,
Second Edition,Morgan Kaufmann, 2005.
http://www.cs.waikato.ac.nz/~ml/weka/book.html
p
Pang-Ning Tan, Michael Steinbach, and Vipin Kumar
Introduction to Data Mining (First Edition)
Addi
Addison
Wesley,
W l (May
(M 2,
2 2005)
http://www-users.cs.umn.edu/~kumar/dmbook/index.php
Dorian Pyle
Data Preparation for Data Mining
Morgan Kaufmann, Mar 15, 1999
Mamdouh Refaat
Data Preparation for Data Mining Using SAS
Morgan Kaufmann, Sep. 29, 2006)
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Data Mining and Soft Computing
Summary
1.
2.
3.
4.
5.
Introduction to Data Mining and Knowledge Discovery
Data Preparation
Introduction to Prediction, Classification, Clustering and Association
Data Mining - From the Top 10 Algorithms to the New Challenges
Introduction to Soft Computing. Focusing our attention in Fuzzy Logic
and Evolutionary Computation
6. Soft Computing Techniques in Data Mining: Fuzzy Data Mining and
Knowledge Extraction based on Evolutionary Learning
7 Genetic
7.
G
ti Fuzzy
F
Systems:
S t
State
St t off the
th Art
A t and
d New
N
Trends
T
d
8. Some Advanced Topics I: Classification with Imbalanced Data Sets
9. Some Advanced Topics II: Subgroup Discovery
10.Some advanced Topics III: Data Complexity
11.Final talk: How must I Do my Experimental Study? Design of
p
in Data Mining/Computational
g
p
Intelligence.
g
Using
g NonExperiments
parametric Tests. Some Cases of Study.