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Data Mining

The Art and Science of Obtaining Knowledge from Data

Dr. Saed Sayad

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

The answer is Data Mining.

What we need?

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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 KnowledgeData Mining

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AI,Machine Learning

Statistics

Data Mining

Database

Data Analysis

Data WarehouseOLAP

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Data MiningData Mining

Data Analysis Database

Statistics Machine Learning Data Warehouse OLAP

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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

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Data Analysis

Classification Regression Clustering Association Sequence Analysis

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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

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Data Analysis (cont.)

Age

Income

Clustering

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

Probability (chips, coke) ?

Association

Sequence Analysis

…ATCTTTAAGGGACTAAAATGCCATAAAAATCCATGGGAGAGACCCAAAAAA…

Xt-1 XtT

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Data Mining in Research Life Cycle

Questions Needs

Search

Research

Experiment

Modeling

Report

Library

Data

Database

Data Analysis

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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.”

Supination Pronation Flexion Extension

Muscle Contraction

Biceps Triceps

Supination H HPronation L LFlexion H LExtension L H

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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

Triceps

Record#

Scatter Plot

Flexion Extension Supination Pronation

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3. Exploration (cont.)

Biceps

Record#

Scatter Plot

Flexion 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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Plastics Extrusion

Plastic pellets

Plastic melt

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Film Extrusion

Extruder

Plastic Film

Defect due to particle

contaminant

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In-Line Monitoring

Transition Piece

Window Ports

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In-Line Monitoring

Light Source Extruder and Interface

Optical Assembly

Imaging Computer

Light

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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 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

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

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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!