Data & Text MiningAbhay Ahluwalia, Chris Bruck, Christopher Stanton, Stefanie Felitto, Mike Paulus

BUAD 466: Introduction to Business Intelligence

November 30, 2011

Data Mining Background

Definition – the process of analyzing data from different perspectives and summarizing it into useful information

Data Mining Software (ex. XL Miner) allows users to analyze data from many different dimensions, categorize it, and summarize the relationships identified

The Basics of Data Mining Analyzes relationships and patterns in stored

transaction data based on open-ended user queries Classes: Stored data is used to locate data in

predetermined groups Clusters: Data items are grouped according to logical

relationships or consumer preferences Associations: Data can be mined to identify

associations Sequential patterns: Data is mined to anticipate

behavior patterns and trends

Text Mining Background Definition: the discovery by computer of

previously unknown knowledge in text, by automatically extracting information from different written resources

Goal: to extract new, never-before encountered information

Text mining can expand the ability of data mining to deal with textual materials

Data are Key to Business Value

DATA: Measures of variables in categories Support Decision Making Provide Basis for Forecasting Important to

Obtain data from new sources (text mining) Integrate (mash) information from multiple sources

Software Example #1: VAIM (Value-Added Information Mash)

MINING: finding patterns in data (pattern-oriented, record-oriented searches)

MASHING: Integrating information mined from multiple resources Useful in Hospitals and for Government Campaigns

Software Example #2: IBM SPSS

Assists in Statistical Analysis in predicting trends

Categorizes data, Preforms Statistical Analysis Multiple Regressions to suggest causality

Software Example #3: XL Miner

Add-In on Microsoft Excel Products Builds off of software that companies already

possess

Assists in predictive forecasting based on observed data trends

Demonstration

Business Value Example #1: Grocery Store

Data mining using Oracle Analyzed buying patterns Finding lead to changes in Marketing Increased revenues

Value Example #2 - University of Rochester Cancer Center Using KnowledgeSEEKER software Studied effect of anxiety of Chemotherapy on

nausea Analysis helped improved treatment of

patients and improved quality of life.

Value Example #3: MGM Grand Hotel Analyzed customer satisfaction and probability

of return stay Found that the front desk and room where most

important Focused next 6 months improving 10% improvement in attrition Increased guest returns and profitability

Business Applications

Pros: Extracts new information

and Combines human linguistic capabilities with the speed and accuracy of a computer

Can answer the ‘Why?’

Competitive advantage

Cons: Expensive

Requires Training

Dependent on structure of warehouses and repositories

Complications & Concerns

Invasion of Privacy According to Lita van Wel and

Lamber Royakkers in “Ethical issues in web data mining”, privacy is considered lost when information about an individual is obtained, used, or spread without that individual’s permission

More Complications• Data is made anonymous

before gathered into profiles, there are no personal profiles; therefore these applications de-individualize the users by judging them just by their mouse clicks

• De-individualization: tendency of judging and treating people on the basis of group characteristics instead of on their own individual characteristics

More Concerns

Companies can claim to collect the data for one purpose and use it for another

The growing movement of selling personal data as a service encourages website owners to trade personal data obtained from their site

The companies that buy the data make it anonymous and these companies and assume ownership of the data that they release

http://www.youtube.com/watch?v=zdM6vzRHrG0

Even More Complications

Some web mining algorithms might use controversial characteristics to categorize individuals, such as sex, race, religion, or sexual orientation This process could result in the refusal of service or a

privilege to an individual based on his race, religion, or sexual orientation.

Application Recommendations & Conclusion

Sync data repositories (VAIM Software) Training Use Data Mining and Text Mining together

Group Jeopardy:Data and

Text Mining Background

Business Applications

Complications with Mining

From the Examples

100 100 100 100

200 200 200 200

300 300 300 300

Data and Text Mining Background For 100:True or False: Clusters refer to Data Items that are grouped according to logical relationships or consumer preferences?

True.

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Data and Text Mining Background For 200:What is the name of the Text Mining Software that allows users to analyze data from different dimensions, categorize it, and summarize the relationships it identified, all within a familiar Microsoft Office Program?

XL Miner

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Data and Text Mining Background For 300:Name either 2 Pro's or 2 Cons to the Business Applications of Data Mining. Pros: extracts new info, can answer the why, creates a

competitive advantage Cons: expensive, requires training, dependent on

structure of warehouses and repositories

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Business Applications for 100:What does VAIM stand for?

Value-Added Information Mashing

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Business Applications for 200:What is the difference between Text Mining and Text Mashing? MINING: finding patterns in data (pattern-oriented,

record-oriented searches) MASHING: Integrating information mined from multiple

resources

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Business Applications for 300:What is the greatest benefit of Text Mining for Businesses? Extracts new information and Combines human

linguistic capabilities with the speed and accuracy of a computer

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Complications for 100:True or False: Companies who buy the data and make it anonymous are not responsible for potential legal actions against them for using the data? False, they are responsible and can have serious legal

actions taken upon them

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Complications for 200:What is the term used when the personal data of individuals is treated on the basis of group characteristics rather than individual characteristics? De-individualization

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Complications for 300:Which two US Senators introduced the Commercial Privacy Bill of Rights? John McCain (R-AZ) John Kerry (D-MA)

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From the Examples for 100:When the grocery store analyzed men's buying trends they found that when men purchased diapers and what other item did they buy?

Beer

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From the Examples for 200:What software did the University of Rochester Cancer Center use to analyze the affects of Chemotherapy treatments on nausea?

KnowledgeSEEKER

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From the Examples for 300:What did Text Mining identify as the two most important areas of the MGM Grand Hotel?

The Front Desk and the Room

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