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??????? Data mining Ch. 1 Introduction

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Data mining Ch. 1 Introduction Major: Interdisciplinary program of the integrated biotechnology Graduate school of bio- & information technology – PowerPoint PPT presentation

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Title: ??????? Data mining Ch. 1 Introduction


1
??????? Data mining Ch. 1 Introduction
  • Major Interdisciplinary program of the
    integrated biotechnology
  • Graduate school of bio- information technology
  • Youngil Lim (N110), Lab. FACS
  • phone 82 31 670 5200 (secretary), 82 31 670
    5207 (direct)
  • Fax 82 31 670 5209, mobile phone 82 10 7665
    5207
  • Email limyi_at_hknu.ac.kr, homepage 
    http//facs.maru.net

2
Outline
Course Course name Time Time Room
  Data mining Thu. 9-12? N130/N116
Overview In recent year, there has been stunning progress in data mining and machine learning. The synthesis of statistics, machine learning, information theory and computing has created a solid science, with a firm mathematical base, and with very powerful tools. This lecture presents the basic theory of automatically extracting models from experimental data, and then validating those models. This lecture includes a multivariate linear regression, training/testing/validation techniques, principle component analysis (PCA), partial least square (PLS) and artificial neural network (ANN) algorithms. Matlab or Weka toolkit is used for computational practices. This lecture is given in English. In recent year, there has been stunning progress in data mining and machine learning. The synthesis of statistics, machine learning, information theory and computing has created a solid science, with a firm mathematical base, and with very powerful tools. This lecture presents the basic theory of automatically extracting models from experimental data, and then validating those models. This lecture includes a multivariate linear regression, training/testing/validation techniques, principle component analysis (PCA), partial least square (PLS) and artificial neural network (ANN) algorithms. Matlab or Weka toolkit is used for computational practices. This lecture is given in English. In recent year, there has been stunning progress in data mining and machine learning. The synthesis of statistics, machine learning, information theory and computing has created a solid science, with a firm mathematical base, and with very powerful tools. This lecture presents the basic theory of automatically extracting models from experimental data, and then validating those models. This lecture includes a multivariate linear regression, training/testing/validation techniques, principle component analysis (PCA), partial least square (PLS) and artificial neural network (ANN) algorithms. Matlab or Weka toolkit is used for computational practices. This lecture is given in English. In recent year, there has been stunning progress in data mining and machine learning. The synthesis of statistics, machine learning, information theory and computing has created a solid science, with a firm mathematical base, and with very powerful tools. This lecture presents the basic theory of automatically extracting models from experimental data, and then validating those models. This lecture includes a multivariate linear regression, training/testing/validation techniques, principle component analysis (PCA), partial least square (PLS) and artificial neural network (ANN) algorithms. Matlab or Weka toolkit is used for computational practices. This lecture is given in English.
Method   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)   Lecture(?), Seminar (?), Computational practice (?),  Factory tour (?), Beam projector(?)
Evaluation   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12   Attendance 8,  homework 20,  Mid-exam 30,  Final-exam 30, Presentation 12
Text Main Witten and Frank, Data mining practical machine learning tools and techniques, Elsevier, 2005. Main Witten and Frank, Data mining practical machine learning tools and techniques, Elsevier, 2005. Main Witten and Frank, Data mining practical machine learning tools and techniques, Elsevier, 2005. Main Witten and Frank, Data mining practical machine learning tools and techniques, Elsevier, 2005.
3
Weekly Lecture Plan
Week Contents Remarks
1 Introduction  
2 EndNote 13, Uses and practices Presentation 1 EndNote 
3 Part I. Machine learning tools and techniques, Ch. 1 what is it all about?  
4 Part I. Machine learning tools and techniques, Ch. 2 input data
5 Part I. Machine learning tools and techniques, Ch. 3 output data
6 Field trip (Factory tour, October 7, 2010) KITECH, Biomass gasifier (Dr. Lee Uen-Do)
7 Part I. Machine learning tools and techniques, Ch. 4 algorithms Presentation 2 ch. 4
8 Part I. Machine learning tools and techniques, Ch. 4 algorithms  
9 Mid-term exam.  
10 Part II. The weka program, Ch. 9 Introduction to Weka
11 Part II. The weka program, Ch. 10 Explorer of Weka ?
12 Field trip (Factory tour)  
13 Part II. The weka program, Ch. 10 Explorer of Weka
14 Ammonia emission problem (Lim et al., 2007)  Analysis of Lim et al. (2007)
15 Final exam. (Report on the ammonia emission problem)  
 
4
Overview of this lecture
output (ch. 3)
input (ch. 2)
Information (data, database)
Data mining (extraction of useful information)
Relationships? Modeling Structural
patterns Technical tools machine learning
Knowledge (understanding, application,
prediction)
  • - Machine learning acquisition of structural
    descriptions automatically or semi-auto.
  • (it is similar as the brain development from
    repeating experiences)
  • Weka written in JAVA (object-oriented
    programming language)
  • (JAVA is free to OS and its calculation is 2-3
    times slower than C, C and Fortran
  • - Java compiler (Java virtual machine) translate
    the byte-code into machine code

5
Outline of this lecture
  • Part I. Machine learning tools and techniques
  • - Level 1 Ch 1. Applications, common problems
  • Ch 2. Input, concepts, instances and
    attributes
  • Ch 3. Output, knowledge
    representation
  • - Level 2 Ch 4. Numerical algorithms, the basic
    methods
  • - Level 3 Ch 5-6 (advanced topics)
  • Part II. Weka manual (ftp//facs/lim/lecture_relat
    ed/weka3.4.exe)
  • - Level 1 Ch 9. Introduction of Weka
  • Ch 10. Explorer
  • - Level 2 Ch 11-15 (advanced options in Weka)

But, you need to read those chapters to make a
paper on data mining
6
Ch. 1. Whats it all about
Life and death It is up to Machine Learning
Cow breeding of farmers - 1/5 cows to be abated -
4/5 cows to be bred What is the decision criteria?
  • Human in vitro fertilization
  • 60 embryos fertilized ? select just 1 embryo
  • What is the decision criteria?

inputs (attributes)
outputs (results)
inputs (attributes)
outputs (results)
  • - age
  • - health
  • calving
  • -

Live or die
- morphology - oocyte -
Live or die
7
1.1 data mining and machine learning
  • Data mining process of discovering structural
    patterns in data.
  • Machine learning technical tools for finding
    structural patterns in data automatically or
    semi-automatically.

Machine learning
Describing structural patterns
  • learning and training
  • training mindless learning
  • Machine learning includes numerical algorithms
    for automatic calculations
  • See Table 1.1
  • there are 4 attributes (descriptors)
  • there are 3 decisions (outputs)
  • 24 possibilities and 24 instances
  • no data missing
  • no noise in data
  • perfect prediction is possible
  • It is an ideal and fictitious example

8
1.1 data mining and machine learning
Table 1.1
9
1.2 sample examples weather problems and others
  • Different datasets tend to expose new issues,
    challenges, and different numerical algorithms
    (case by case).

2. Contact lenses ideal case
1. The weather problem
  • machine learning is to
  • identify the data structure
  • predict for new cases
  • see Table 1.1, Figure 1.1, and Figure 1.2
  • which representation is better understandable
    between Figure 1.1 and Figure 1.2?
  • See Table 1.2
  • there are 4 attributes (descriptors)
  • there are 2 decisions (outputs)
  • 36 possibilities and 14 instances
  • decision list in order (see p11)
  • numeric-attribute problem
  • mixed-attribute problem
  • The classification rule is one of the
  • association rules, and it is the best rule.

In Ch. 3, We will learn more classification/associ
ation rules
10
1.2 Examples
Table 1.2
Table 1.3
11
1.2 Examples
Figure 1.1
Figure 1.2 Decision Tree
12
1.2 sample examples weather problems and others
3. Irises numerical dataset
4. CPU performance numeric prediction
  • See Table 1.4 (Fisher, 1935)
  • there are 4 attributes
  • numeric-attribute problem
  • see p16.
  • - Output is the 3 categories
  • More compact rule is in Ch. 3.
  • That is we use the following statement
  • if then
  • else if then
  • end if
  • see Table 1.5
  • there are 6 attributes
  • Output is estimated by multi-variable linear
    regression (MLR)
  • prediction of numeric performance
  • Numerical algorithms will be reviewed in ch. 4

13
1.2 examples
Table 1.4
14
1.2 examples
Table 1.5
15
1.2 sample examples weather problems and others
5. Labor negotiation
  • see Table 1.6
  • there are 16 attributes and 40 examples
  • missing data but realistic case
  • 2 decisions (acceptable or not)
  • see Figure 1.3 (a) and (b)
  • (a) simple and intuitive decision tree
  • (b) complex and accurate representation
  • which is the artifact and overfited?
  • Ch. 5 and 6 concern about cross-validation and
    missing data

6. Soybean classification
  • see Table 1.7
  • a successful story of ML techniques
  • soybean disease diagnosing
  • 35 attributes and 680 examples
  • 19 outputs (categories)
  • 97.5 accuracy over 72 of expert
  • the expert adopted ML rules

16
1.2 examples
Table 1.6
17
1.2 examples
Table 1.7
18
1.3 Fielded applications
The previous examples are speculative and toy
problems. Where is the beef?
1. Decision of loan company
  • for borderline applicants of loan (sub-prime
    loan)
  • there are about 20 attributes
  • there are 2 decisions (accept or not)
  • the borrowers pay off or default
  • correct predictions from ML are related to the
    profit of the loan company.

2. Screening satellite images for oil slicks
detection
  • For warning ecological disasters
  • oil slicks appear as dark regions in the image
  • the detection is an expensive manual process
  • this problem is challenged because
  • scarcity of training data
  • a very small fraction are actual oil slicks
  • batch process (case by case)

19
1.3 Fielded applications
3. Power load forecasting in electricity supply
industry
  • for estimating Max. and Min. of load for hour,
    day, month, season, and year
  • there are many attributes (day/night, holiday,
    weekend, weather )
  • we need a dynamic model
  • ML system is far quicker than the trained human
    forecasters.
  • a few seconds or a few days

4. Diagnosis of machines and devices
  • For determination of the kind of fault,
  • the diagnosis process is too labor intensive
  • 1000 different devices, and noisy data
  • outputs are 600 faults
  • low level attributes from vibration records of
    devices
  • derived attributes from Fouriers analysis
  • ML performance is slightly superior to that of
    expert

20
1.3 Fielded applications
5. Marketing and sales
  • for planning store layouts, special discounts,
    offering coupons,
  • attributes costumers purchase records (by
    membership card)
  • ? market basket analysis
  • econometrics
  • detecting customers who is fickle and defect

6. Other applications
  • control problems of plants
  • biology identification of genes
  • biomedicine prediction of drug activity, and 3D
    structure
  • astronomy
  • chemistry structure identification of certain
    organic compounds
  • automation

21
1.4 Machine learning and statistics
What is the difference between ML and statistics?
- ML statistics marketing - Statistics ML
that has arisen out of computer science - But,
two perspectives have converged - Statistical
tests are used to validate ML models and to
evaluate ML algorithms
22
1.6 data mining and ethics
Data mining is used for people, it provokes
ethical problems such as racial, sexual and
religious
23
1.5 Generalization as search
ML and statistics generalization as search
This section is optional, as indicated by the
gray bar !
24
Field trip report (as a scientific report)
  • Components or contents
  • Introduction backgrounds, states-of-art, aims,
    and short overview of the report
  • Main body Knowledge or information from field
    trips, applications, results, and analyses
  • Conclusion summary, and perspectives
  • Reference books, papers, patents, reports,
    websites
  • Appendix accessory information

25
An example of field trip report
  • Application of data mining to a process in
    Petro-chemical company, Samsung Total
  • Miso Kim (misokim_at_hknu.ac.kr), 200720111
  • Dept. Chemical engineering, Hankyong National
    University
  • Gyonggi-do Anseong Jungangno 167, 456-749 Korea
  • 1. Introduction
  • 1.1 What is data mining?
  • 1.2 Aims of this report
  • 1.3 Overview of this report
  • 2. Main processes of Petro-chemical plant of
    Samsung Total.
  • 2.1 PE and PP processes
  • 2.2 BTX processes
  • 2.3
  • Each table and each figure have own number and
    title. Those tables and figures should be well
    explained in the text.
  • 3. Application of data mining tools
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