MIS 111: Computers and the Inter-networked Society

Class 11: Data Mining

July 25th, 2011

This Week Data Mining (Today):

humans and machines generate knowledge from data

Decision Support Systems (Tuesday) combining models and data in an attempt to

solve semistructured and some unstructured problems with extensive user involvement

Expert Systems (Machine’s that make decisions) Computer systems that attempt to mimic human

experts by applying expertise in a specific domain.

Learning Objectives List a few current events in information

systems news Recap of quiz 2 learning objectives Use Google analytics to perform data mining

and make business decisions List 3 practical applications of data mining Explain the difference between Descriptive

and Predictive data mining Compare and contrast classification,

association rule, deviation detection data mining

List a tool that can help you perform data mining

Administrative Trivia Quiz

Some people didn’t put their name on their quiz

If you have a zero, come talk with me We’ll go over it together today

Assignment 3 due Wednesday morning before class

Quiz 2 Recap http://eller.qualtrics.com/SE/?SID=SV_eaGLk1J

Gq9kczBy

Data Mining

What exactly IS Data Mining?

Roughly speaking, Data Mining is the process by which humans and machines generate knowledge from data. Data Warehouse

Data Processing

Data Analytics

What is Data Mining?

Many Definitions Non-trivial extraction of implicit, previously

unknown and potentially useful information from data

Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns

Knowledge Discovery in Databases

Data Mining is only a small part of the knowledge discovery process. Which part of the process do you think is most

critical? Which part of the process do you think takes

longest?

What is (not) Data Mining?

What is Data Mining?

– Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area)

– Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,)

What is not Data Mining?

– Look up phone number in phone directory

– Query a Web search engine for information about “Amazon”

Lots of data is being collected and warehoused Web data, e-commerce purchases at department/

grocery stores Bank/Credit Card

transactions

Computers have become cheaper and more powerful Competitive Pressure is Strong

Provide better, customized services for an edge (e.g. in Customer Relationship Management)

Why Mine Data? Commercial Viewpoint

Why Mine Data? Scientific Viewpoint

Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite telescopes scanning the skies microarrays generating gene

expression data scientific simulations

generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists

in classifying and segmenting data in Hypothesis Formation

Evolution of Data AnalysisEvolutionary Step

Data Collection(1960s)

Data Access(1980s)

DataWarehousing &Decision Support(1990s)

Data Mining(2000s)

Business Question

"What was my total

revenue in the last

five years?"

"What were unitsales in NewEngland last

March?"

"What were unitsales in NewEngland last

March? Drill downto Boston."

"What’s likely tohappen to Boston

unit sales nextmonth? Why?"

EnablingTechnologies

Computers, tapes,disks

Relationaldatabases(RDBMS)

On-line analyticprocessing(OLAP),

multidimensionaldatabases, data

warehouses

Advancedalgorithms,

multiprocessorcomputers, massive

databasesinformation

delivery

In what disciplines do people use data mining?

Google Analytics

AnimalLingo.com Google Analytics What in here is data mining:

Map Overlay Time series analysis New visits Bounce rate Time on site …

What changes should I make to my Web site (this is getting into the role of decision support systems)?

E-commerce

http://www.amazon.com/

Finance

Finance Basic: Finance.yahoo.com Can be much, much more complex (I should

have a finance PhD student come in) IS data mining and finance are a great mix!

Some examples ahead (you don’t have to know these unless you want to)

Finance: Portfolio Management (FYI)1. Collect 30- 40 historical fundamental and technical factors for stock

S1, say for 10-20 years.2. Build a neural network NN1 for predicting the return values for stock

S1.3. Repeat steps 1 and 2 for every stock Si,that is monitored by the

investor. Say 3000 stocks are monitored and 3000 networks, NNi are generated.

4. Forecast stock return Si (t + k)for each stock i and k days ahead (say a week, seven days) by computing NNi(Si(t))=S(t+k).

5. Select n highest Si (t + k)values of predicted stock return.6. Compute a total forecasted return of selected stocks, T and compute

Si(t+k)/T. Invest to each stock proportionally to Si(t+k)/T.DATA MINING FOR FINANCIAL APPLICATIONS 13

7. Recompute NNi model for each stock i every k days adding new arrived data to the training set. Repeat all steps for the next portfolio adjustment.

http://www.math.nsc.ru/AP/ScientificDiscovery/PDF/data_mining_for_financial_applications.pdf

http://en.wikipedia.org/wiki/File:Neural_network_example.svg

Interpretable Trading Rules (FYI) Categorical rules predict a categorical

attribute, such as increase/decrease, buy/sell.

Discovering Fraud (FYI) http://www.picalo.org/

Sports

Sports and Data Mining Go for it on forth down!

http://www.advancednflstats.com/2009/09/4th-down-study-part-1.html

Moneyball: The Art of Winning an Unfair Game

The Main Message of Moneyball

By analyzing baseball statistics you could see through a lot of baseball nonsense.

For instance, when baseball managers talked about scoring runs, they tended to focus on team batting average, but if you ran the analysis you could see that the number of runs a team scored bore little relation to that team's batting average. It correlated much more exactly with a team's on-base and slugging percentages.

Other applications

Just some examples… Linguistics Economics Farming Government Defense / homeland ssecurity Education Production forecasting Sales forecasting Fast food Just about ANY DISCIPLINE can benefit from

data mining

Data Mining Tasks

Data Mining Tasks Prediction Methods

Use some variables to predict unknown or future values of other variables.

Description Methods Find human-interpretable patterns that describe

the data.

From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

Data Mining Tasks... Classification [Predictive]

Clustering [Descriptive]

Association Rule Discovery [Descriptive]

Sequential Pattern Discovery [Descriptive]

Regression [Predictive]

Deviation Detection [Predictive]

Lots of tools to help: Weka R SPSS SAS Google Picalo Google Correlate / Graphs

Classification

Classification: Definition Given a collection of records (training set )

Each record contains a set of attributes, one of the attributes is the class.

Find a model for class attribute as a function of the values of other attributes.

Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy

of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

Classification Example

Tid Refund MaritalStatus

TaxableIncome Cheat

1 Yes Single 125K No

2 No Married 100K No

3 No Single 70K No

4 Yes Married 120K No

5 No Divorced 95K Yes

6 No Married 60K No

7 Yes Divorced 220K No

8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10

cate

gorica

l

cate

gorica

l

contin

uous

class

Refund MaritalStatus

TaxableIncome Cheat

No Single 75K ?

Yes Married 50K ?

No Married 150K ?

Yes Divorced 90K ?

No Single 40K ?

No Married 80K ?10

TestSet

Training Set

ModelLearn

Classifier

FYI: LOTS of different Classification Algorithms

Neural network Mimics the way

that humans Learn with NEURONS

Decision trees K-means

clustering

Classification: The Iris Flower Data Set Which factors help us determine which Iris

type a flower will be?

Petal Length Petal Width Sepal Length Sepal Width

We can make the machine “learn” which attributes = which iris types.

Personal Equity Plan Weka Example

Classification: A Business Application Direct Marketing

Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product.

Approach: Use the data for a similar product introduced before. We know which customers decided to buy and which

decided otherwise. This {buy, don’t buy} decision forms the class attribute.

Collect various demographic, lifestyle, and company-interaction related information about all such customers.

Type of business, where they stay, how much they earn, etc.

Use this information as input attributes to learn a classifier model.

From [Berry & Linoff] Data Mining Techniques, 1997

Association Rule

Association Rule Discovery: Definition Given a set of records each of which contain some

number of items from a given collection; Produce dependency rules which will predict

occurrence of an item based on occurrences of other items.

TID Items

1 Bread, Coke, Milk

2 Beer, Bread

3 Beer, Coke, Diaper, Milk

4 Beer, Bread, Diaper, Milk

5 Coke, Diaper, Milk

Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer}

Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer}

Association Rule Discovery: A Business Application

Supermarket shelf management. Goal: To identify items that are bought

together by sufficiently many customers.

Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items.

A classic rule -- If a customer buys diaper and milk,

then he is very likely to buy beer.So, don’t be surprised if you find six-

packs stacked next to diapers!

Application of Association Rule Discovery: Shelf-Management

Correlations and time http://correlate.googlelabs.com/tutorial Tree IPad and Apple stock

Any problems with the association rule? Correlation does not cause causation

Deviation/Anomaly Detection

Deviation/Anomaly Detection

Detect significant deviations from normal behavior Applications:

Credit Card Fraud Detection

Network Intrusion Detection

Typical network traffic at University level may reach over 100 million connections per day

Deviation/Anomaly Detection What kinds of deviations from normal

behavior might you want to mine for?

For Credit Card Fraud Detection Picalo!

For Network Intrusion Detection

Applications and Issues

Recommender Systems

Use Data Mining techniques to recommend products/services based on user behavior

Use Data Mining techniques to recommend products/services based on user specification

Data Mining: Some Concerns

Is your interpretation valid?

Do you have enough data to process for “good” results?

Finally… should the government be able to data mine?

Data Mining is NOT MAGIC• Data Mining will not do any of the following:

• Automatically find answers to questions you do not ask

• Constantly monitor your database for new and interesting relationships

• Eliminate the need to understand your business and your data

• Remove the need for good data analysis skills

“The Great Giveaway” 1. In what way did Amazon, Ebay, and Google “expose

themselves?”

2. Why did they make the decision to open their doors?

3. How might this move “change business in ways they don’t understand?”

4. How are “hacker entrepreneurs” taking advantage of this move?

5. Do you think that this move was a smart one for Amazon, Google, and Ebay? Why or why not?

6. Pretend that you’re a “hacker entrepreneur. What kind of application are you going to develop with all of that data?

Tomorrow’s class Read:

http://en.wikipedia.org/wiki/Decision_support_system

Bring your computer and excel (meet here and we’ll move to a room with computers halfway through class)