Introduction to Data Mining

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Introduction to Data Mining
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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

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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.
• ATT
• 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.

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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.

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Data Mining

• 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
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What is (not) Data Mining?

• What is Data Mining?
• Certain names are more prevalent in certain
  locations (OBrien, ORurke, OReilly in Boston
  area)
• Discover groups of similar documents on the Web

• What is not Data Mining?
• Look up phone number in phone directory
• Query a Web search engine for information about
  Amazon

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Origins of Data Mining

• Draws ideas from machine learning/AI,
  statistics, and database systems

Statistics
Machine Learning
Data Mining
Database systems
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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

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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.

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Learning

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

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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.

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Memory

• Okay. Let's say we observe our neighbor on three
  days

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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 Casual Drive
15 Snow Mon Casual Walk
-5 Snow Mon Casual
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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 Casual Drive
15 Snow Mon Casual Walk
-5 Snow Mon Casual Drive
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Noisy Data

• Things arent 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
25 None Sat Casual Walk
25 None Sat Casual Drive
25 None Sat Casual Drive
25 None Sat Casual Walk
25 None Sat Casual Walk
25 None Sat Casual Walk
25 None Sat Casual ?

• 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.

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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
25 None Sat Casual Walk
25 None Sat Casual Drive
25 None Sat Casual Drive
25 None Sat Casual Walk
25 None Sat Casual Walk
25 None Sat Casual Walk
25 None Sat Casual Walk
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Generalization

• 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

• Dealing with previously unseen cases
• Will she walk or drive?

Temp Precip Day Clothes
22 None Fri Casual Walk
3 None Sun Casual Walk
10 Rain Wed Casual Walk
30 None Mon Casual Drive
20 None Sat Formal Drive
25 None Sat Casual Drive
-5 Snow Mon Casual Drive
27 None Tue Casual Drive
24 Rain Mon Casual ?
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Classification Another Example
categorical
categorical
continuous
class
Learn Classifier
Training Set
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Example of a Decision Tree
Splitting Attributes
Refund
Yes
No
MarSt
NO
Married
Single, Divorced
TaxInc
NO
lt 80K
gt 80K
YES
NO
Model Decision Tree
Training Data
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Apply Model to Test Data
Test Data
Start from the root of tree.
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Apply Model to Test Data
Test Data
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Apply Model to Test Data
Test Data
Refund
Yes
No
MarSt
NO
Married
Single, Divorced
TaxInc
NO
lt 80K
gt 80K
YES
NO
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Apply Model to Test Data
Test Data
Refund
Yes
No
MarSt
NO
Married
Single, Divorced
TaxInc
NO
lt 80K
gt 80K
YES
NO
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Apply Model to Test Data
Test Data
Refund
Yes
No
MarSt
NO
Married
Single, Divorced
TaxInc
NO
lt 80K
gt 80K
YES
NO
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Apply Model to Test Data
Test Data
Refund
Yes
No
MarSt
NO
Assign Cheat to No
Married
Single, Divorced
TaxInc
NO
lt 80K
gt 80K
YES
NO
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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, dont 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.

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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.

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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 gt30 M subscribers (0.5 is
  150,000 customers).

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Assessing Credit Risk

• Situation Person applies for a loan
• Task Should a bank approve the loan?
• People who have the best credit dont need the
  loans
• People with worst credit are not likely to repay.
  
• Banks 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.

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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?

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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.

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Beer and Diapers

• Whats the explanation here?

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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.

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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.

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Genes

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

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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.

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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.

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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).