data mining - university of...

Data Mining

Dr. Saed SayadUniversity of Toronto

2010

[email protected]

1http://chem-eng.utoronto.ca/~datamining/

Data Mining

http://chem-eng.utoronto.ca/~datamining/ 2

Data mining is about explaining the past and predicting the

future by means of data analysis.


AI &Machine Learning

Statistics

Data Mining

Database & DW

Data Mining


0 10 20 30 40 50 60

Gambling

Entertainment/ Music

Investment / Stocks

Junk email / Anti-spam

Security / Anti-terrorism

Travel/Hospitality

Web

Biotech/Genomics

e-Commerce

Other

Government applications

Medical/ Pharma

Health care/ HR

Science

Manufacturing

Telecom

Insurance

Retail

Fraud Detection

Direct Marketing/ Fundraising

Credit Scoring

Banking

CRM

Data Mining Applications

Source: KDnuggets.com


much higher20%

somewhat higher

30%

about the same41%

somewhat lower4%

much lower5%

Data mining activity in 2007 compare to 2006

Source: KDnuggets.com

Data Mining Steps

1 • Problem Definition

2 • Data Preparation

3 • Data Exploration

4 • Modeling

5 • Evaluation

6 • Deployment


CRISP-DM Process ModelCRoss-Industry Standard Process for Data Mining


Source: http://www.crisp-dm.org/Process/index.htm

1. Problem Definition


Understanding the project objectives and requirements from a business perspective and then converting this knowledge into a data mining problem definition with a preliminary plan designed to achieve the objectives.

Source: http://www.crisp-dm.org/Process/index.htm

2. Data Preparation

Modeling Data

DataText

Data DSN

ETL


3. Data Exploration

Data Exploration

UnivariateAnalysis

Average, StDev, Min, Max, ...

Bar, Line, Pie, ...

Charts

Bivariate Analysis

Correlation

Z test, ...

Combination Charts


Data Exploration - Univariate


Data Exploration - Bivariate


4. Modeling

Classification

Bayesian

Decision Tree

Logistic Regression

SVM

Regression

Linear Regression

Robust Regression

Neural Network

Clustering

Hierarchical

K-Means

Association

A Priori


Data Mining: Classification & Regression


Frequency

Table

OneR

Bayesian

Decision Tree

Markov Chains

HMM

Covariance

Matrix

Linear

Regression

LDA

(Z Score)

PCA/PCR

Logistic

Regression

Robust Regression

Similarity

Functions

KNN

Neural

Networks

Perceptron

Back

Propagation

RBF

Others

SVM

GA

Scalable Methods

Modeling - Classification


fAge Responder

e.g., Y or N

Modeling - Regression


fAge AmountPurchased

e.g., $350

Modeling - Clustering


Age

Income

Association Rules


Market Basket Analysis

5. Evaluation

Charts Stats

Variables Contribution

Mean Square Error

Confusion Matrix

K-S Chart

Lift Chart

Gain Chart


Evaluation - Confusion Matrix


True

Positive

False

Positive

False

Negative

True

Negative

CM

Positive Cases Negative Cases

Pre

dic

ted

Po

siti

veP

red

icte

d

Neg

ativ

e

Evaluation – Gain Chart


Population%

50%10%

100%

100%

45%

10%

Responder%

6. Deployment

SQL VB

JAVA HTML


Data Mining Team

Modeler

AnalystDBA


DomainExpert

Data Mining Software Vendors


Data Mining

SAS

KXEN

KNIMEAngoss

SPSS

Case Study...


data mining - university of...

Documents