The Art and Technology of Data Mining

University of Toronto04/11/23 1

Data Mining

The Art and Science of Obtaining Knowledge from Data

Dr. Saed Sayad


Agenda

Explosion of data Introduction to data mining Examples of data mining in science

and engineering Challenges and opportunities


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


Explosion of Data (cont.)


Fast, accurate, and scalable data analysis techniques to extract useful knowledge:

The answer is Data Mining.

What we need?


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 KnowledgeData Mining


AI,Machine Learning

Statistics

Data Mining

Database

Data Analysis

Data WarehouseOLAP


Data MiningData Mining

Data Analysis Database

Statistics Machine Learning Data Warehouse OLAP


Text Files Relational Database

Multi-dimensional 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

Database


Data Analysis

Classification Regression Clustering Association Sequence Analysis


Data Analysis

X1

X2 Y2

Output Variablesor

Targets

Y1Numeric

Categorical

Numeric

Categorical

Regression (0,1)

Classification (good, bad)

age, income, …

gender, occupation, …

Linear Modelsor

Decision Trees

Input Variablesor

Attributes

ModelModel

W1

W2


Data Analysis (cont.)

Age

Income

Clustering

1, chips, coke, chocolate2, gum, chips3, chips, coke4, …

Probability (chips, coke) ?

Association

Sequence Analysis

…ATCTTTAAGGGACTAAAATGCCATAAAAATCCATGGGAGAGACCCAAAAAA…

Xt-1 XtT


Data Mining in Research Life Cycle

Questions Needs

Search

Research

Experiment

Modeling

Report

Library

Data

Database

Data Analysis


Data Mining – Modeling Steps

1.Problem Definition

2.Data Preparation

3.Exploration

4.Modeling

5.Evaluation

6.Deployment


Agenda

Explosion of data Introduction to data mining Examples of data mining in science and

engineering Challenges and opportunities


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”


1. Problem Definition“Control a robotic arm by means of EMG signals from biceps and triceps muscles.”

Supination Pronation Flexion Extension

Muscle Contraction

Biceps Triceps

Supination H HPronation L LFlexion H LExtension L H


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.


3. Exploration

Triceps

Record#

Scatter Plot

Flexion Extension Supination Pronation


3. Exploration (cont.)

Biceps

Record#

Scatter Plot

Flexion Extension Supination Pronation


5. Modeling

Classification

OneR Decision Tree Naïve Bayesian K-Nearest Neighbors Neural Networks Linear Discriminant Analysis Support Vector Machines …


6. Model Deployment

A neural network model was successfully implemented inside the robotic arm.


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”


Plastics Extrusion

Plastic pellets

Plastic melt


Film Extrusion

Extruder

Plastic Film

Defect due to particle

contaminant


In-Line Monitoring

Transition Piece

Window Ports


In-Line Monitoring

Light Source Extruder and Interface

Optical Assembly

Imaging Computer

Light


Melt Without Contaminant Particles (WO)


Melt With Contaminant Particles (WP)


1. Problem Definition

Classify images into those with particles (WP) and those without particles (WO).

WO WP


2. Data Preparation

2000 Images

54 Input variables all numeric

One output variables with two possible values-With Particle -Without Particle


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)


3. Exploration

Demo!


4. Modeling

Classification:

• OneR• Decision Tree• 3-Nearest Neighbors• Naïve Bayesian


5. Evaluation

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

10 -fold cross-validation

If pixel_density_max < 142 then WP


6. Deploy model A Visual Basic program will be developed to implement the model.


Agenda

Explosion of data Introduction to data mining Examples of data mining in science &

engineering Challenges and opportunities


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.


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!

The Art and Technology of Data Mining

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