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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1
Data Mining: Introduction
Lecture Notes for Chapter 1
Introduction to Data Mining
by
Tan, Steinbach, Kumar
O Lots of data is being collected
and warehoused
- Web data, e-commerce
- purchases at department/
grocery stores
transactions
O Computers have become cheaper and more powerful
O 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
O Data collected and stored at
enormous speeds (GB/hour)
- remote sensors on a satellite
- telescopes scanning the skies
- microarrays generating gene
expression data
generating terabytes of data
O Traditional techniques infeasible for raw data
O Data mining may help scientists
- in classifying and segmenting data
- in Hypothesis Formation
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 4
Mining Large Data Sets - Motivation
O There is often information “hidden” in the data that is
not readily evident
O Human analysts may take weeks to discover useful
information
O Much of the data is never analyzed at all
0
500,
1,000,
1,500,
2,000,
2,500,
3,000,
3,500,
4,000,
1995 1996 1997 1998 1999
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”
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 7
O Draws ideas from machine learning/AI, pattern
recognition, statistics, and database systems
O Traditional Techniques
may be unsuitable due to
- Enormity of data
- High dimensionality
of data
distributed nature
of data
Origins of Data Mining
Machine Learning/
Pattern
Recognition
Statistics/
AI
Data Mining
Database
systems
Data Mining Tasks
O Prediction Methods
- Use some variables to predict unknown or
future values of other variables.
O Description Methods
- Find human-interpretable patterns that
describe the data.
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 9
Data Mining Tasks...
O Classification [Predictive]
O Clustering [Descriptive]
O Association Rule Discovery [Descriptive]
O Sequential Pattern Discovery [Descriptive]
O Regression [Predictive]
O Deviation Detection [Predictive]
Classification: Definition
O Given a collection of records ( training set )
- Each record contains a set of attributes , one of the
attributes is the class.
O Find a model for class attribute as a function
of the values of other attributes.
O 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.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 13
Classification: Application 2
O Fraud Detection
- Goal: Predict fraudulent cases in credit card
transactions.
Use credit card transactions and the information on its
account-holder as attributes.
- When does a customer buy, what does he buy, how often he 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.
Classification: Application 3
O Customer Attrition/Churn:
- Goal: To predict whether a customer is likely
to be lost to a competitor.
Use detailed record of transactions with each of the
past and present customers, to find attributes.
- 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.
From [Berry & Linoff] Data Mining Techniques, 1997
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 15
Classification: Application 4
O 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
Classifying Galaxies
Early
Intermediate
Late
Data Size:
- 72 million stars, 20 million galaxies
- Object Catalog: 9 GB
- Image Database: 150 GB
Class:
Attributes:
- Image features,
- Characteristics of light
waves received, etc.
Courtesy: http://aps.umn.edu
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 19
Clustering: Application 1
O 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.
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.
Clustering: Application 2
O Document Clustering:
- Goal: To find groups of documents that are
similar to each other based on the important
terms appearing in them.
- Approach: To identify frequently occurring
terms 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 or search
term to clustered documents.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 21
Illustrating Document Clustering
O Clustering Points: 3204 Articles of Los Angeles Times.
O Similarity Measure: How many words are common in
these documents (after some word filtering).
Category Total
Articles
Correctly
Placed
Financial 555 364
Foreign 341 260
National 273 36
Metro 943 746
Sports 738 573
Entertainment 354 278
Clustering of S&P 500 Stock Data
Discovered Clusters Industry Group
Applied-Matl-DOW N,Bay-Net work-Down,3-COM-DOWN, Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN, DSC-Co mm-DOW N,INTEL-DOWN,LSI-Logic-DOWN, Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down, Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOW N, Sun-DOW N
Technology1-DOWN
Apple-Co mp-DOW N,Autodesk-DOWN,DEC-DOWN, ADV-M icro-Device-DOWN,Andrew-Corp-DOWN, Co mputer-Assoc-DOWN,Circuit-City-DOWN, Co mpaq-DOWN, EM C-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOW N,Microsoft-DOWN,Scientific-Atl-DOWN
Technology2-DOWN
Fannie-Mae-DOWN,Fed-Ho me-Loan-DOW N, MBNA-Corp -DOWN,Morgan-Stanley-DOWN (^) Financial-DOWN
Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP, Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP, Schlu mberger-UP
Oil-UP
] Observe Stock Movements every day.
] Clustering points: Stock-{UP/DOWN}
] Similarity Measure: Two points are more similar if the events
described by them frequently happen together on the same day.
] We used association rules to quantify a similarity measure.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 25
Association Rule Discovery: Application 2
O 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.
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!
Association Rule Discovery: Application 3
O Inventory Management:
- Goal: A consumer appliance repair company wants to
anticipate the nature of repairs on its consumer
products and keep the service vehicles equipped with
right parts to reduce on number of visits to consumer
households.
- Approach: Process the data on tools and parts
required in previous repairs at different consumer
locations and discover the co-occurrence patterns.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 27
Sequential Pattern Discovery: Definition
O Given is a set of objects , with each object associated with its own timeline of
events , find rules that predict strong sequential dependencies among
different events.
O Rules are formed by first disovering patterns. Event occurrences in the
patterns are governed by timing constraints.
(A B) (C) (D E)
<= ms
<= xg (^) >ng <= ws
(A B) (C) (D E)
Sequential Pattern Discovery: Examples
O In telecommunications alarm logs,
- (Inverter_Problem Excessive_Line_Current)
(Rectifier_Alarm) --> (Fire_Alarm)
O In point-of-sale transaction sequences,
(Intro_To_Visual_C) (C++_Primer) -->
(Perl_for_dummies,Tcl_Tk)
(Shoes) (Racket, Racketball) --> (Sports_Jacket)
Challenges of Data Mining
O Scalability
O Dimensionality
O Complex and Heterogeneous Data
O Data Quality
O Data Ownership and Distribution
O Privacy Preservation
O Streaming Data
