Download The Art and Technology of Data Mining

Data Mining
The Art and Science of
Obtaining Knowledge from Data
Dr. Saed Sayad
5/22/2017
University of Toronto
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Agenda
 Explosion of data
 Introduction to data mining
 Examples of data mining in science
and engineering
 Challenges and opportunities
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Explosion of Data
 Data in the world doubles every 20 months!
 NASA’s Earth Orbiting System:
46 megabytes of data per second
4,000,000,000,000 bytes a day
 FBI fingerprints image library:
200,000,000,000,000 bytes
 In-line image analysis for particle detection:
1 megabyte in one second
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Explosion of Data (cont.)
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Explosion of Data (cont.)
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Explosion of Data (cont.)
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Explosion of Data (cont.)
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What we need?
Fast, accurate, and scalable data
analysis techniques to extract useful
knowledge:
The answer is Data Mining.
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What is Data Mining?
“Data Mining is the exploration and
analysis of large or small quantities of
data in order to discover meaningful
patterns, trends and rules.”
Data
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Data Mining
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Knowledge
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Data Analysis
AI,
Machine
Learning
Statistics
Data Mining
Database
Data Warehouse
OLAP
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Data Mining
Data Analysis
Statistics
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Database
Machine Learning
Data Warehouse
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OLAP
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Database
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Text Files
Relational
Database
Multidimensional
Database
Entities
File
Table
Cube
Attributes
Row and
Col
Record, Field,
Index
Dimension,
Level,
Measurement
Methods
Read,
Write
Select, Insert,
Update,
Delete
Drill down,
Drill up, Drill
through
Language
-
SQL
MDX
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Data Analysis
 Classification
 Regression
 Clustering
 Association
 Sequence Analysis
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Data Analysis
Numeric
X1
W1
Numeric
Regression
age, income, …
Categorical
Y1
X2
W2
Model
(0,1)
Y2
Categorical
Classification
gender, occupation, …
(good, bad)
Input Variables
or
Attributes
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Linear Models
or
Decision Trees
University of Toronto
Output Variables
or
Targets
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Data Analysis (cont.)
Clustering
Association
Income
1, chips, coke, chocolate
2, gum, chips
3, chips, coke
4, …
Age
Probability (chips, coke) ?
Sequence Analysis
…ATCTTTAAGGGACTAAAATGCCATAAAAATCCATGGGAGAGACCCAAAAAA…
Xt-1
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Xt
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Data Mining in Research Life Cycle
 Questions
 Needs
Report
Library
Search
Data
Analysis
Modeling
Database
Research
Data
Experiment
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Data Mining – Modeling Steps
1.Problem Definition
2.Data Preparation
3.Exploration
4.Modeling
5.Evaluation
6.Deployment
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Agenda
 Explosion of data
 Introduction to data mining
 Examples of data mining in science and
engineering
 Challenges and opportunities
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Examples of data mining in science & engineering
1. Data mining in Biomedical Engineering
“Robotic Arm Control Using Data Mining Techniques”
2. Data mining in Chemical Engineering
“Data Mining for In-line Image Monitoring of
Extrusion Processing”
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1. Problem Definition
“Control a robotic arm by means of EMG signals
from biceps and triceps muscles.”
Muscle
Contraction
Biceps
Triceps
Supination
H
L
H
L
Flexion
H
L
Extension
L
H
Pronation
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Supination Pronation
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Flexion
Extension
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2. Data Preparation
The dataset includes 80 records.
 There are two input variables; biceps
signal and triceps signal.
 One output variable, with four possible
values; Supination, Pronation, Flexion and
Extension.
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3. Exploration
Scatter Plot
Triceps
Record#
Flexion
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Extension Supination Pronation
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3. Exploration (cont.)
Scatter Plot
Biceps
Record#
Flexion
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Extension Supination Pronation
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5. Modeling

Classification
 OneR
 Decision Tree
 Naïve Bayesian
 K-Nearest Neighbors
 Neural Networks
 Linear Discriminant Analysis
 Support Vector Machines
…
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6. Model Deployment
A neural network model was successfully
implemented inside the robotic arm.
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Examples of data mining in science & engineering
1. Data mining in Biomedical Engineering
“Robotic Arm Control Using Data Mining Techniques”
2. Data mining in Chemical Engineering
“Data Mining for In-line Image Monitoring of Extrusion
Processing”
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University of Toronto
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Plastics Extrusion
Plastic
pellets
Plastic melt
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Film Extrusion
Defect due to
particle
contaminant
Extruder
Plastic Film
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In-Line Monitoring
Transition
Piece
Window
Ports
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In-Line Monitoring
Optical Assembly
Light
Light Source
Extruder and
Interface
Imaging
Computer
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Melt Without Contaminant Particles (WO)
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Melt With Contaminant Particles (WP)
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1. Problem Definition
Classify images into those with particles (WP)
and those without particles (WO).
WO
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WP
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2. Data Preparation
 2000 Images
 54 Input variables all numeric
 One output variables with two possible values
-With Particle
-Without Particle
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2. Data Preparation (cont.)
 Pre-processed images to remove noise
 Dataset 1 with sharp images: 1350 images
including 1257 without particles and 91 with particles
 Dataset 2 with sharp and blurry images: 2000
images including 1909 without particles and blurry
particles and 91 with particles
 54 Input variables, all numeric
 One output variable, with two possible values (WP
and WO)
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3. Exploration
Demo!
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4. Modeling
Classification:
• OneR
• Decision Tree
• 3-Nearest Neighbors
• Naïve Bayesian
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5. Evaluation
10 -fold cross-validation
Dataset
Attrib.
Class
One-R
C4.5
3.N.N
Bayes
Sharp
Images
54
2
99.9
99.8
99.8
95.8
Sharp +
Blurry
Images
54
2
98.5
97.8
97.8
93.3
Sharp +
Blurry
Images
54
3
87
87
84
79
If pixel_density_max < 142 then WP
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6. Deploy model
 A Visual Basic program will be developed to implement the model.
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Agenda
 Explosion of data
 Introduction to data mining
 Examples of data mining in science &
engineering
 Challenges and opportunities
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Challenges and Opportunities
 Data mining is a ‘top ten’ emerging technology.
 High pay job! in the financial, medical and
engineering.
 Faster, more accurate and more scalable
techniques.
 Incremental, on-line and real-time learning
algorithms.
 Parallel and distributed data processing
techniques.
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Data mining is an exciting and
challenging field with the ability to
solve many complex scientific and
business problems.
You can be part of the solution!
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