Data Mining and Soft Computing

Francisco HerreraResearch Group on Soft Computing andInformation Intelligent Systems (SCI2S) g y ( )

Dept. of Computer Science and A.I. University of Granada, Spain

Email: herrera@decsai.ugr.eshttp://sci2s.ugr.eshttp://sci2s.ugr.es

http://decsai.ugr.es/~herrera

Data Mining and Soft ComputingData Mining and Soft Computing

In this course:

We will introduce the Data Mining and K l d Di t t kKnowledge Discovery area: steps, task, challenges ..

We will introduce Soft Computing techniques: p g qFuzzy Logic, Genetic Algorithms, …

… and we will present the use of Soft Computing 2tecniques in Data Mining

Data Mining and Soft ComputingData Mining and Soft Computing

Material of this course at: http://sci2s.ugr.es/docencia/asignatura.php?id_asignatura=14

3

Data Mining and Soft ComputingData mining:

Data Mining and Soft ComputingData mining:

Extraction of interesting (non-trivial, implicit, i l k d t ti ll f l)previously unknown and potentially useful)

information or patterns from data in large databasesK l d

Patterns

Knowledge

Targetd t

Processed

Patterns

data data

InterpretationEvaluation

data

P i

Data Mining Evaluation

4Selection

Preprocessing& cleaning

Data MiningData Mining

How can I analyze this data?

Knowledge

We have rich data, but poor information

Data mining-searching for knowledge (interesting patterns) in your data.

5

g p y

J. Han, M. Kamber. Data Mining. Concepts and Techniques Morgan Kaufmann, 2006 (Second Edition)

S f C iSoft computing refers to a collection of computational techniques in

Soft ComputingSoft 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 ti l th d h t i ld d l t l ti dmore conventional methods have not yielded low cost, analytic, and

complete solutions.

Prof. Zadeh:

"...in contrast to traditional hard computing, soft computing exploits the tolerance for

i i i i d i l himprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution-

cost, and better rapport with reality”, pp yLotfi A. Zadeh

Introduce “Fuzzy Logic” in 1965

6and “Soft Computing”

in 1992.

C t ti l I t lliComputational Intelligence

The Field of Interest of the Society shall be the theory, d i li ti d d l t f bi l i lldesign, application, and development of biologically

and linguistically motivated computational paradigms emphasizing neural networks connectionist systemsemphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy

systems, and hybrid intelligent systems in which these y , y g yparadigms are contained.

7

Data Mining and Soft ComputingData Mining and Soft Computing

Contents: P t I P i i l f D t Mi iPart I. Principles of Data Mining

Introd ction to Data Mining and Kno ledgeIntroduction 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

8

Challenges

Data Mining and Soft ComputingData Mining and Soft Computing

Contents: P t II S ft C ti T h i i D t Mi iPart II. Soft Computing Techniques in Data Mining

Introd ction to Soft Comp ting Foc sing o rIntroduction to Soft Computing. Focusing our attention in Fuzzy Logic and Evolutionary ComputationComputation

Soft Computing Techniques in Data Mining: FuzzySoft Computing Techniques in Data Mining: Fuzzy Data Mining and Knowledge Extraction based on Evolutionary LearningEvolutionary Learning

Genetic Fuzzy Systems: State of the Art and New 9Trends

Data Mining and Soft ComputingData Mining and Soft Computing

Contents: P t III D t Mi i S Ad d T iPart III. Data Mining: Some Advanced Topics

Some Ad anced Topics I Classification ithSome Advanced Topics I: Classification with 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/

10Computational Intelligence. Using Non-parametric Tests. Some Cases of Study.

BibliographyBibliographyJ Han M KamberJ. Han, M. 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.htmlp

Pang-Ning Tan, Michael Steinbach, and Vipin Kumar Introduction to Data Mining (First Edition)

Addi W l (M 2 2005)Addison Wesley, (May 2, 2005)http://www-users.cs.umn.edu/~kumar/dmbook/index.php

Dorian Pyle Data Preparation for Data MiningMorgan Kaufmann, Mar 15, 1999

Mamdouh Refaat

11

Mamdouh RefaatData Preparation for Data Mining Using SAS

Morgan Kaufmann, Sep. 29, 2006)

Data Mining and Soft Computing

Summary1. Introduction to Data Mining and Knowledge Discovery2. Data Preparation 3. Introduction to Prediction, Classification, Clustering and Association3. Introduction to Prediction, Classification, Clustering and Association4. Data Mining - From the Top 10 Algorithms to the New Challenges5. Introduction to Soft Computing. Focusing our attention in Fuzzy Logic

and Evolutionary Computationand Evolutionary Computation6. Soft Computing Techniques in Data Mining: Fuzzy Data Mining and

Knowledge Extraction based on Evolutionary Learning7 G ti F S t St t f th A t d N T d7. Genetic Fuzzy Systems: State of the Art and New Trends8. Some Advanced Topics I: Classification with Imbalanced Data Sets9. Some Advanced Topics II: Subgroup Discovery10.Some advanced Topics III: Data Complexity 11.Final talk: How must I Do my Experimental Study? Design of

Experiments in Data Mining/Computational Intelligence. Using Non-p g p g gparametric Tests. Some Cases of Study.