Download Steven F. Ashby Center for Applied Scientific Computing Month DD

CISC 4631
Data Mining
Lecture 01:
Introduction to Data Mining
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Let’s Start By Seeing What You Know
• Quick Quiz
– Do you know what Data Mining is?
– Do you know of any examples of Data Mining?
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What is Data Mining?
• Data Mining has 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
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Alternative Names
• Data Mining was/is known by these other names
(although many of these have lost favor over time):
–
–
–
–
Knowledge discovery in databases (KDD)
Knowledge extraction
Data/pattern analysis
Data archeology, data dredging, information harvesting,
business intelligence, etc.
• Recently introduced new names (maybe with different
emphases):
– Data Science
– Big Data
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Some Examples
• Netflix and Amazon use data mining to recommend
products (recommender systems)
• Companies use data mining for marketing
– Who should be mailed a catalog
– Who should see what online ads (Google Adwords)
• Fordham’s WISDM project uses smartphone
accelerometer data to classify user activities (walking,
jogging, sitting, etc.)
• Some search engines cluster retrieved documents into
meaningful groups
– Group pages about Jaguar into “car” pages and “cat” pages
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Why Data Mining and Why Now?
• Data Mining was not very popular until about
10 – 15 years ago
Quick Quiz: What do
you think changed?
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Why Mine Data?
• There are now tremendous amounts of data
that are automatically collected and
warehoused. What are some examples?
– Web data, e-commerce
– Store purchases
– Bank/Credit Card transactions
– Cell phone GPS information
– Smartphone and Smartwatch Sensor Data
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Why Mine Data?
• What technological changes have helped make data
mining so prevalent now?
– Computers: cheaper and more powerful
• Smaller mobile devices are exploding in popularity
– Disk and other storage: greater capacity and cheaper
– Increased use of on-line resources and Internet
• We shouldn’t discount the advances in algorithms
but most data mining algorithms are relatively
mature
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Why Mine Data?
• In business, competitive pressure is strong
– Provide better, customized services for an edge
(e.g. in Customer Relationship Management)
– CRM is a relatively big deal now
• How do we get the most out of the customer over the
long run
• Example: Customer Churn Analysis
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Why Mine Data?
• Often info “hidden” in data is not evident
• Analysts may take weeks to discover useful
information
• Much of the data is never analyzed at all
– There is just too much data to analyze without
“assistance”
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Scientific Need
• Data collected at enormous speeds
– remote sensors on satellite
– telescopes scanning the skies
– microarrays generating gene
expression data
– scientific simulations
• Traditional techniques infeasible
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How Big is the Data?
• Examples of Large Data Sets
– AT&T’s 26TB call detail database (2003)
– Ebay 6PB, IRS 150TB data warehouse
– Yahoo has a 2PB DB to analyze behavior of ½ billion
web visitors/month (24 billion events/day)
– Wal-Mart has a 583 TB database (2006)
– Indexed web contains about 20 Billion pages
– Sites like Facebook, Flicker & Twitter contain lots of
data
• Google is estimated (in 2011) to have 900,000
servers to handle its data!
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How Much Data is Being Created?
• 5 Exabytes new data created (2002, UC Berkeley)
• Humans created/copied 161/281 Exabytes in 06/07 (IDC)
–
–
–
–
1 Exabyte = 1018
12 stacks of books stretching from Earth to Sun
3 million times the books ever written
Not all data stored at once (includes temporary data)
• In 2012 2.8 ZB (2800EB) of data will be created/copied
– Forecast for 2020: 40 ZB, or (57X number of grains of sand on Earth)
OK, we get the point
already.! Head hurts.
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Why Data Mining? Why Now?
According to BabyCenter.com,
today one in three children born in
the United States already have an
online presence (usually in the
form of a sonogram) before they
are born. That number grows to
92% by the time they are two. In
2012 the average digital birth of
children occurs at approximately six
months, with a third of all
children’s photos and information
posted online within weeks of their
birth. What will it mean to live in a
world where our every moment,
from birth to death, is digitally
chronicled and preserved in vast
cloud based databases, forever?
During the first day of a baby’s life, the amount of data generated by humanity
is equivalent to 70 times the information contained in the library of congress.
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Origins of Data Mining
• Draws ideas from machine learning/AI, pattern
recognition, statistics, and database systems*
• Traditional techniques
Artificial Intelligence
may be unsuitable due to
Statistics Machine Learning
– Enormity of data
– High dimensionality
– Heterogeneous & distributed data
* databases currently have limited impact;
data mining is rarely done in a database but
rather on “flat files”
Pattern Recognition
Data Mining
Database
systems
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Statistics vs. Data Mining
• Experience has shown that students with
statistics backgrounds are often confused by
data mining if the differences aren’t highlighted
• When compared to Data Mining:
• Statistics is more theory-based
– Data mining methods are often based on heuristic algorithms
– Statistics is based firmly on mathematics (e.g., probability)
• Statistics is more focused on testing hypotheses vs.
finding interesting relationships
• Statistics makes more assumptions about the data
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The Process of Data Mining
Data Mining is a process, sometimes referred to as a knowledge
discovery process. In this process there is a data mining step that
applies data mining algorithms to extract knowledge. About 80% of
our class will focus on the data mining step but in the real world 80%
of the time is spent on the other steps (e.g., prepping data)
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Second Part of Introduction:
DATA MINING TASKS
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Top-Level Data Mining Tasks
• At highest level, data mining tasks can be
divided into:
– Prediction Tasks (supervised learning)
• Use some variables to predict unknown or future
values of other variables
– Description Tasks (unsupervised learning)
• Find human-interpretable patterns that describe the
data
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Key Data Mining Tasks
• Overview of the major data mining tasks
studied in this course:
– Prediction Tasks
• Classification
• Regression
– Description Tasks
• Clustering
• Association Rule Discovery
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Classification: Definition
• Given a collection of records (training set )
– Each record contains a set of attributes, one of the attributes is the
class, which is to be predicted.
• Find a model for class attribute as a function of
the values of other attributes.
– Model maps record to a class value
• Goal: previously unseen records should be
assigned a class as accurately as possible.
– A test set is used to determine accuracy of the model
• Can you think of classification tasks?
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Classification Example
Tid Refund Marital
Status
Taxable
Income Cheat
Refund Marital
Status
Taxable
Income Cheat
1
Yes
Single
125K
No
No
Single
75K
?
2
No
Married
100K
No
Yes
Married
50K
?
3
No
Single
70K
No
No
Married
150K
?
4
Yes
Married
120K
No
Yes
Divorced 90K
?
5
No
Divorced 95K
Yes
No
Single
40K
?
6
No
Married
No
No
Married
80K
?
60K
10
7
Yes
Divorced 220K
No
8
No
Single
85K
Yes
9
No
Married
75K
No
10
10
No
Single
90K
Yes
Training
Set
Learn
Classifier
Test
Set
Model
Classification: Application 1
• 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 companyinteraction related information about all such customers.
– Type of business, where they stay, how much they earn, etc.
• Use this info as input attributes to learn a classifier model
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Classification: Application 2
• Fraud Detection
– Goal: Predict fraudulent cases in credit card transactions
– Approach:
• Use credit card transactions and info on account-holders
as attributes
– When and what does customer buy, how often pays on time, etc
• Label 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: Application 3
• Sky Survey Cataloging
– Goal: To predict class (star or galaxy) of sky objects,
especially visually faint ones, based on the telescopic
survey images (from Palomar Observatory).
– 3000 images with 23,040 x 23,040 pixels per image.
– Approach:
•
•
•
•
Segment the image.
Measure image attributes (features) - 40 of them per object.
Model the class based on these features.
Success Story: Could find 16 new high red-shift quasars, some of
the farthest objects that are difficult to find!
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
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Classifying Galaxies
Courtesy: http://aps.umn.edu
Early
Class:
• Stages of Formation
Attributes:
• Image features,
• Characteristics of light
waves received, etc.
Intermediate
Late
Data Size:
• 72 million stars, 20 million galaxies
• Object Catalog: 9 GB
• Image Database: 150 GB
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Regression
• Predict a value of a given continuous (numerical)
variable based on the values of other variables
• Greatly studied in statistics
• Examples:
– Predicting sales amounts of new product based on
advertising expenditure.
– Predicting wind velocities as a function of temperature,
humidity, air pressure, etc.
– Time series prediction of stock market indices
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Clustering
• Given a set of data points find clusters so that
– Data points in same cluster are similar
– Data points in different clusters are dissimilar
You try it on the Simpsons. How can
we cluster these 5 “data points”?
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What is a natural grouping among these objects?
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What is a natural grouping among these objects?
Clustering is subjective
Simpson's Family
School Employees
Females
Males
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What is Similarity?
The quality or state of being similar; likeness; resemblance; as, a similarity of features.
Webster's Dictionary
Similarity is hard
to define, but…
“We know it
when we see it”
The real meaning
of similarity is a
philosophical
question. We will
take a more
pragmatic
approach.
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Clustering: Application 1
• Market Segmentation:
– Goal: subdivide a market into distinct subsets of
similar customers
– Approach:
• Collect different attributes of customers based on
their geographical and lifestyle related information.
• Find clusters of similar customers.
• Measure the clustering quality by observing buying
patterns of customers in same cluster vs. those from
different clusters.
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Clustering: Application 2
• Document Clustering:
– Goal: Find groups of documents that are similar to
each other based on the words appearing in them
– Approach: Identify frequently occurring terms in
each document. Form a similarity measure based
on the frequencies of different terms. Use it to
cluster.
– Uses: Information Retrieval can utilize the clusters
to relate a new document or search term to
clustered documents.
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Association Rule Discovery
• 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.
Rules Discovered:
TID
Items
1
2
3
4
5
Bread, Coke, Milk
Beer, Bread
Beer, Coke, Diaper, Milk
Beer, Bread, Diaper, Milk
Coke, Diaper, Milk
{Milk} --> {Coke}
{Diaper, Milk} --> {Beer}
Diapers
beer
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Association Rule Discovery
Application
• Marketing and Sales Promotion Applications
– Let the rule discovered be
{Bagels, … } --> {Potato Chips}
– Potato Chips as consequent => Can be used to determine what
should be done to boost its sales.
– Bagels in the antecedent => Can be used to see which products
would be affected if the store discontinues selling bagels.
– Bagels in antecedent and Potato chips in consequent => Can be
used to see what products should be sold with Bagels to
promote sale of Potato chips!
• Can help determine where to position store items
– Supermarket shelf management
– Did you ever notice that some stores have bananas in the
cereal aisle?
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Challenges of Data Mining
•
•
•
•
•
•
•
Scalability
Dimensionality
Complex and Heterogeneous Data
Data Quality
Data Ownership and Distribution
Privacy Preservation
Streaming Data
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What is (and is not) Data Mining?
• Based on the definitions of data mining, are these
DM or not?
– Finding a phone number in a directory
• Not data mining (trivial?, DB query)
– Grouping related documents returned by search engine
• Is data mining (not trivial, clustering)
– Identifying who has a disease based on symptoms
• Is data mining (not trivial, classification)
– Web search on keyword using search engine
• May be data mining**
** More of an information retrieval task than data mining task. However,
since Google does much more than keyword matching, there will be a
data mining component. For example, Google mines the link structure
of the Web to decide which pages are important (link mining is a type
of data mining).
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If you are Interested in Data
Mining
• Data sets
– NYC open data (https://nycopendata.socrata.com/)
– UCI Data Repository (http://archive.ics.uci.edu/ml/)
• Visit kdnuggets, an online newsletter and more
– http://www.kdnuggets.com
– You can arrange to have newsletter emailed to you
– Also includes job openings
• ACM SIGKDD is the professional organization associated with
data mining
– ACM Special Interest Group (SIG) on data mining
– Can join SIGKDD for $22 or for $54 can also join ACM as student
member
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