1. Introduction to Data Mining

2. Why Mine Data? Commercial Viewpoint
Lots of data is being collected and warehoused
Web data, e-commerce
purchases at department/ grocery stores
Bank/Credit Card transactions
Twice as much information was created in 2002 as in 1999 (~30% growth rate)
Other growth rate estimates even higher

3. Largest databases in 2007
Largest database in the world: World Data Centre for Climate (WDCC) operated by the Max Planck Institute and German Climate Computing Centre
220 terabytes of data on climate research and climatic trends,
110 terabytes worth of climate simulation data.
6 petabytes worth of additional information stored on tapes.
AT&T
323 terabytes of information
1.9 trillion phone call records
Google
91 million searches per day,
After a year worth of searches, this figure amounts to more than 33 trillion database entries.

4. Why Mine Data? Scientific Viewpoint
Data is collected and stored at enormous speeds (GB/hour). E.g.
remote sensors on a satellite
telescopes scanning the skies
scientific simulations generating terabytes of data
Very little data will ever be looked at by a human
Knowledge Discovery is NEEDED to make sense and use of data.

5. Data Mining The Data Gap
Data mining is the process of automatically discovering useful information in large data repositories.
Human analysts may take weeks to discover useful information.
Much of the data is never analyzed at all.
The Data Gap
Total new disk (TB) since 1995
Number of analysts
From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”

6. What is (not) Data Mining?
Look up phone number in phone directory
Query a Web search engine for information about “Amazon”
What is Data Mining?
Certain names are more prevalent in certain locations (O’Brien, O’Rurke, O’Reilly… in Boston area)
Discover groups of similar documents on the Web

7. Origins of Data Mining
Draws ideas from: machine learning/AI, statistics, and database systems
Statistics
Machine Learning
Data Mining
Database systems

8. Data Mining Tasks
Data mining tasks are generally divided into two major categories:
Predictive tasks [Use some attributes to predict unknown or future values of other attributes.]
Classification
Regression
Deviation Detection
Descriptive tasks [Find human-interpretable patterns that describe the data.]
Association Discovery
Clustering

9. Predictive Data Mining or Supervised learning
Given a collection of records (training set)
Each record contains a set of attributes, one of the attributes is the class.
Find ("learn") a model for the class attribute as a function of the values of the other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.

10. Learning
We can think of at least three different problems being involved in learning:
memory,
averaging, and
generalization.

11. Example problem (Adapted from Leslie Kaelbling's example in the MIT courseware)
Imagine that I'm trying predict whether my neighbor is going to drive into work, so I can ask for a ride.
Whether she drives into work seems to depend on the following attributes of the day:
temperature,
expected precipitation,
day of the week,
what she's wearing.

12. Memory Okay. Let's say we observe our neighbor on three days: Clothes
Shop
Day
Precip
Temp
Walk
Casual No
Sat
None 25
Drive
Casual
Yes
Mon
Snow -5
Walk
Casual
Yes
Mon
Snow 15

13. Memory
Now, we find ourselves on a snowy “–5” – degree Monday, when the neighbor is wearing casual clothes and going shopping.
Do you think she's going to drive?
Temp
Precip
Day
Clothes 25
None
Sat
Casual
Walk -5
Snow
Mon
Drive 15

14. Memory The standard answer in this case is "yes".
This day is just like one of the ones we've seen before, and so it seems like a good bet to predict "yes."
This is about the most rudimentary form of learning, which is just to memorize the things you've seen before.
Temp
Precip
Day
Clothes 25
None
Sat
Casual
Walk -5
Snow
Mon
Drive 15

15. Noisy Data
Things aren’t always as easy as they were in the previous case. What if you get this set of noisy data?
Temp
Precip
Day
Clothes 25
None
Sat
Casual
Walk
Drive ?
Now, we are asked to predict what's going to happen.
We have certainly seen this case before.
But the problem is that it has had different answers. Our neighbor is not entirely reliable.

16. Averaging One strategy would be to predict the majority outcome.
The neighbor walked more times than she drove in this situation, so we might predict "walk".
Temp
Precip
Day
Clothes 25
None
Sat
Casual
Walk
Drive

17. Generalization Dealing with previously unseen cases
We might plausibly make any of the following arguments:
She's going to walk because it's raining today and the only other time it rained, she walked.
She's going to drive because she has always driven on Mondays…
Generalization
Dealing with previously unseen cases
Will she walk or drive?
Temp
Precip
Day
Clothes 22
None
Fri
Casual
Walk 3
Sun 10
Rain
Wed 30
Mon
Drive 20
Sat
Formal 25 -5
Snow 27
Tue 24 ?

18. Classification Another Example
categorical
categorical
continuous
class
Test
Set
Learn
Classifier
Model
Training
Set

19. Example of a Decision Tree
categorical
continuous
class
Splitting Attributes
Refund
Yes No NO
MarSt
Single, Divorced
Married
TaxInc NO
< 80K
> 80K NO
YES
Training Data
Model: Decision Tree

20. Apply Model to Test Data
Start from the root of tree.
Refund
MarSt
TaxInc
YES NO
Yes No
Married
Single, Divorced
< 80K
> 80K

21. Apply Model to Test Data
Refund
MarSt
TaxInc
YES NO
Yes No
Married
Single, Divorced
< 80K
> 80K

22. Apply Model to Test Data
Refund
Yes No NO
MarSt
Single, Divorced
Married
TaxInc NO
< 80K
> 80K NO
YES

23. Apply Model to Test Data
Refund
Yes No NO
MarSt
Single, Divorced
Married
TaxInc NO
< 80K
> 80K NO
YES

24. Apply Model to Test Data
Refund
Yes No NO
MarSt
Single, Divorced
Married
TaxInc NO
< 80K
> 80K NO
YES

25. Apply Model to Test Data
Refund
Yes No NO
MarSt
Married
Assign Cheat to “No”
Single, Divorced
TaxInc NO
< 80K
> 80K NO
YES

26. Classification: 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 other related information about all such customers. E.g.
Type of business,
where they stay,
how much they earn, etc.
Use this information as input attributes to learn a classifier model.

27. Classification: Fraud Detection
Goal: Predict fraudulent cases in credit card transactions.
Approach:
Use credit card transactions and the information associated with them as attributes, e.g.
when does a customer buy,
what does he buy,
where does he buy, etc.
Label some past transactions as fraud or fair transactions. This forms the class attribute.
Learn a model for the class of the transactions.
Use this model to detect fraud by observing credit card transactions on an account.

28. Classification: Attrition/Churn
Situation: Attrition rate for mobile phone customers is around 25-30% a year!
Goal: To predict whether a customer is likely to be lost to a competitor.
Approach:
Use detailed record of transactions with each of the past and present customers, to find attributes. E.g.
how often the customer calls,
where he calls,
what time-of-the day he calls most,
his financial status,
marital status, etc.
Label the customers as loyal or disloyal. Find a model for loyalty.
Success story (Reported in 2003):
Verizon Wireless performed this kind of data mining reducing attrition rate from over 2% per month to under 1.5% per month.
Huge impact, with >30 M subscribers (0.5% is 150,000 customers).

29. Assessing Credit Risk Situation: Person applies for a loan
Task: Should a bank approve the loan?
People who have the best credit don’t need the loans
People with worst credit are not likely to repay.
Bank’s best customers are in the middle
Banks develop credit models using a variety of data mining methods.
Mortgage and credit card proliferation are the results of being able to "successfully" predict if a person is likely to default on a loan.
Widely deployed in many countries.

30. Frequent-Itemset Mining (Association Discovery)
The Market-Basket Model
A large set of items, e.g., things sold in a supermarket.
A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys on one day.
Fundamental problem
What sets of items are often bought together?
Application
If a large number of baskets contain both hot dogs and mustard, we can use this information in several ways. How?

31. Hot Dogs and Mustard
Apparently, many people walk from where the hot dogs are to where the mustard is.
We can put them close together, and put between them other foods that might also be bought with hot dogs and mustard, e.g., ketchup or potato chips.
Doing so can generate additional "impulse" sales.
The store can run a sale on hot dogs and at the same time raise the price of mustard.
People will come to the store for the cheap hot dogs, and many will need mustard too.
It is not worth the trouble to go to another store for cheaper mustard, so they buy that too.
The store makes back on mustard what it loses on hot dogs, and also gets more customers into the store.

32. Beer and Diapers
What’s the explanation here?

33. On-Line Purchases
Amazon.com offers several million different items for sale, and has several tens of millions of customers.
Basket = Customer, Item = Book, DVD, etc.
Motivation: Find out what items are bought together.
Basket = Book, DVD, etc. Item = Customer
Motivation: Find out similar customers.

34. Words and Documents
Baskets = sentences; items = words in those sentences.
Lets us find words that appear together unusually frequently, i.e., linked concepts.
Baskets = sentences, items = documents containing those sentences.
Items that appear together too often could represent plagiarism.

35. Genes Baskets = people; items = genes or blood-chemistry factors.
Has been used to detect combinations of genes that result in diabetes

36. Clustering
Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that
Data points in one cluster are more similar to one another.
Data points in separate clusters are less similar to one another.
Similarity Measures:
Euclidean Distance if attributes are continuous.
Other Problem-specific Measures.
E.g. Euclidean Distance Based Clustering in 3-D space.
Intracluster distances
are minimized
Intercluster distances
are maximized

37. Clustering: Application 1
Market Segmentation:
Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix.
Approach:
Collect different attributes of customers based on their geographical and lifestyle related information.
Find clusters of similar customers.

38. Clustering: Application 2
Document Clustering:
Goal: To find groups of documents that are similar to each other based on the important words appearing in them.
Approach:
Identify frequently occurring words in each document.
Form a similarity measure based on the frequencies of different terms. Use it to cluster.
Gain: Information Retrieval can utilize the clusters to relate a new document to clustered documents.
There are two natural clusters in the data set.
The first cluster consists of the first four articles, which correspond to news about the economy.
The second cluster contains the last four articles, which correspond to news about health care.
Each article is represented as a set of word-frequency pairs (w, c).