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Introduction to Data Mining

Jiang Li

Department of Computer Science & InformationTechnology

Austin Peay State University

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Outline

Data Collected

Knowledge Discovery An IterativeProcess

Data Mining Examples

Data Mining Functions and Algorithms

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Data Collected

Business

Wal-Mart

20 million transactions a day

Mobile Oil Corporation

A 100 terabytes data warehouse

Science

The human genome database project

Gigabytes of data

NASA Earth Observing System (EOS)

50 gigabytes data per hour

Radio, Television, and Film Studios Multimedia databases

WWW the infinite resources

Email huge digital libraries

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Data vs. Knowledge

Technology is available to help us collect data Bar code, cameras, scanners, Radars, satellites, etc.

Technology is available to help us store data Databases, data warehouses, variety of repositories

We are swamped by data that pours on us We need to interpret this data in search for new knowledge

Our need is to extract interesting

knowledge (rules, regularities,

patterns, constraints) from data in

large collections.

We are drowning in information, but starving for

knowledge.

John Naisbitt

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Evolution of Database Technology

1960s: Data collection, database creation (hierarchical and

network models)

1970s:

Relational data model, relational DBMSimplementation

1980s: Ubiquitous RDBMS, advanced data models

(extended-relational, Object-Oriented, deductive,etc.) and application-oriented DBMS (spatial,scientific, engineering, etc.)

1990s: Data mining and data warehousing, multimedia

databases, and Web-based database technology

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Knowledge Discovery

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Steps of a KDD Process

Learning the application domain relevant prior knowledge and goals of

application

Gathering and integrating of data

Cleaning and preprocessing data (maytake 60% of effort!)

Reducing and projecting data Find useful features, dimensionality/variable

reduction,

Choosing mining functions and algorithms summarization, classification, regression,

association,

Data mining: search for patterns of interest

Evaluating results Interpretation: analysis of results

visualization, alteration, removingredundant patterns,

Use of discovered knowledge

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Data Mining On What Kind ofData?

Flat Files

Generic Data Relational & Object-Relational Databases

Object-Oriented Databases

Multimedia Data Text Databases

Audio, Image, and Video Databases

Business Data Transactional Databases

Engineering Data Spatial databases

Temporal and Time-series databases

WWW Data

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Data Mining Examples

Data mining is primarily used today bycompanies with a strong consumer focus - retail,financial, communication, and marketingorganizations. It enables these companies to determine relationships

among "internal" factors such as price, productpositioning, or staff skills, and "external" factors suchas economic indicators, competition, and customerdemographics.

And, it enables them to determine the impact on sales,customer satisfaction, and corporate profits.

Finally, it enables them to "drill down" into summary

information to view detail transactional data.

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Data Mining Examples

With data mining, a retailer could use point-of-salerecords of customer purchases to send targetedpromotions based on an individual's purchase history. By mining demographic data from comment or warranty cards,

the retailer could develop products and promotions to appealto specific customer segments.

Blockbuster Entertainment mines its video rentalhistory database to recommend rentals to individualcustomers.

American Express can suggest products to itscardholders based on analysis of their monthlyexpenditures.

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Data Mining Examples

WalMart is pioneering massive data mining to transformits supplier relationships.

WalMart captures point-of-sale transactions from over 2,900stores in 6 countries and continuously transmits this data to

its massive 7.5 terabyte Teradata data warehouse.

WalMart allows more than 3,500 suppliers, to access data ontheir products and perform data analyses.

These suppliers use this data to identify customer buying

patterns at the store display level.

They use this information to manage local store inventoryand identify new merchandising opportunities.

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Business Data Mining Examples

The NBA is exploring a data mining application that can beused in conjunction with image recordings of basketballgames.

The Advanced Scout software analyzes the movements ofplayers to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game

played between the New York Knicks and the Cleveland Cavalierson January 6, 1995 reveals that when Mark Price played theGuard position, John Williams attempted four jump shots andmade each one!

A coach can automatically bring up the video clips showing each

of the jump shots attempted by Williams with Price on the floor,without needing to comb through hours of video footage.

Those clips show a very successful pick-and-roll play in whichPrice draws the Knick's defense and then finds Williams for anopen jump shot.

http://www.research.ibm.com/scout/home.htmlhttp://www.research.ibm.com/scout/home.html

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Data Mining Functions andAlgorithms

Association Rules

Data can be mined to identify associations. The butter->bread example is an example of associative mining.

To find rules like inside(x, city) near(x, highway).

Classification and Prediction Classify data based on the values in a classifying attribute, e.g.,

classify countries based on climate

classify cars based on gas mileage

Stored data is used to locate data in predetermined groups.

A restaurant chain could mine customer purchase data to determinewhen customers visit and what they typically order. This information

could be used to increase traffic by having daily specials.

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Data Mining Functions andAlgorithms

Clustering

Data items are grouped according to logical relationships or

consumer preferences. Data can be mined to identify market segments or consumer

affinities.

To cluster houses to find distribution patterns.

Sequential patterns

Data is mined to anticipate behavior patterns and trends. An outdoor equipment retailer could predict the likelihood of a

backpack being purchased based on a consumer's purchase ofsleeping bags and hiking shoes.

To find and characterize similar sequences and deviation data,

e.g., stock analysis. To find segment-wise or total cycles or periodic behaviors in

time-related data.

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Data Mining Linear Classification

D e b t

I n c o m e

L o a n

N o L o a n

$ T

A simple linear classification boundary for the loan dataset: shaded region denotes class no loan

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Data Mining - Confluence of MultipleDisciplines