1) In your own words, define “data miningâ€
2) Suppose that you are employed as a data mining consultant for an Internet search engine company. Describe how data mining can help the company by giving specific examples of how techniques, such as clustering, classification, association rule mining, and anomaly detection can be applied. (provide at least 1 example for each technique)
In order to receive full credit for the initial discussion post, you must include at least two citations (APA) from academic resources
Data Mining: Introduction
Lecture Notes for Chapter 1
Introduction to Data Mining
by
Tan, Steinbach, Kumar
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
Mining Large Data Sets – Motivation
There is often information “hidden†in the data that is
not readily evident
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
disks
Units Capacity PBs
1995 89,054 104.8
1996 105,686 183.9
1997 129,281 343.63
1998 143,649 724.36
1999 165,857 1394.6
2000 187,835 2553.7
2001 212,800 4641
2002 239,138 8119
2003 268,227 13027
1995 104.8
1996 183.9
1997 343.63
1998 724.36
1999 1394.6
2000 2553.7
2001 4641
2002 8119
2003 13027
disks
0
0
0
0
0
0
0
0
0
chart data gap
26535 105700
27229 227400
27245 425330
27309 891970
25953 1727000
chart data gap 2
26535 105700
27229 333100
27245 758430
27309 1650400
25953 3377400
data gap
Ph.D. Petabytes Terabytes Total TBs PBs
1995 105.7 105700 105700 105.7
1996 227.4 227400 333100 333.1
1997 425.33 425330 758430 758.43
1998 891.97 891970 1650400 1650.4
1999 1727 1727000 3377400 3377.4
2000 5792 5792000 9169400 9169.4
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Science and engineering Ph.D.s, total 22,868 24,023 24,675 25,443 26,205 26,535 27,229 27,245 27,309 25,953
105700 333100 758430 1650400 3377400
105700 333100 758430 1650400 3377400
Sheet3
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
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 “Amazonâ€
Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems
Traditional Techniques
may be unsuitable due to
Enormity of data
High dimensionality
of data
Heterogeneous,
distributed nature
of data
Origins of Data Mining
Machine Learning/
Pattern
Recognition
Statistics/
AI
Data Mining
Database systems
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]
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
categorical
categorical
continuous
class
Training
Set
Learn
Classifier
Tid
Refund
Marital
Status
Taxable
Income
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
Yes
10
Refund
Marital
Status
Taxable
Income
Cheat
No
Single
75K
?
Yes
Married
50K
?
No
Married
150K
?
Yes
Divorced
90K
?
No
Single
40K
?
No
Married
80K
?
10
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 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
Classification: Application 2
Fraud Detection
Goal: Predict fraudulent cases in credit card transactions.
Approach:
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
Customer Attrition/Churn:
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.
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
Classification: Application 4
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:
Stages of Formation
Attributes:
Image features,
Characteristics of light waves received, etc.
Courtesy: http://aps.umn.edu
Clustering Definition
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.
Illustrating Clustering
Euclidean Distance Based Clustering in 3-D space.
Intracluster distances
are minimized
Intercluster distances
are maximized
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.
Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters.
Clustering: Application 2
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.
Illustrating Document Clustering
Clustering Points: 3204 Articles of Los Angeles Times.
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
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.
Discovered Clusters
Industry Group
1
Applied-Matl-DOWN,Bay-Network-Down,3-COM-DOWN,
Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN,
DSC-Comm-DOWN,INTEL-DOWN,LSI-Logic-DOWN,
Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down,
Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOWN,
Sun-DOWN
Technology1-DOWN
2
Apple-Comp-DOWN,Autodesk-DOWN,DEC-DOWN,
ADV-Micro-Device-DOWN,Andrew-Corp-DOWN,
Computer-Assoc-DOWN,Circuit-City-DOWN,
Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN,
Motorola-DOWN,Microsoft-DOWN,Scientific-Atl-DOWN
Technology2-DOWN
3
Fannie-Mae-DOWN,Fed-Home-Loan-DOWN,
MBNA-Corp-DOWN,Morgan-Stanley-DOWN
Financial-DOWN
4
Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP,
Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP,
Schlumberger-UP
Oil-UP
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.
Rules Discovered:
{Milk} –> {Coke}
{Diaper, Milk} –> {Beer}
TID
Items
1
Bread, Coke, Milk
2
Beer, Bread
3
Beer, Coke, Diaper, Milk
4
Beer, Bread, Diaper, Milk
5
Coke, Diaper, Milk
Association Rule Discovery: Application 1
Marketing and Sales Promotion:
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!
Association Rule Discovery: Application 2
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!
Association Rule Discovery: Application 3
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.
Sequential Pattern Discovery: Definition
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.
Rules are formed by first disovering patterns. Event occurrences in the patterns are governed by timing constraints.
Sequential Pattern Discovery: Examples
In telecommunications alarm logs,
(Inverter_Problem Excessive_Line_Current)
(Rectifier_Alarm) –> (Fire_Alarm)
In point-of-sale transaction sequences,
Computer Bookstore:
(Intro_To_Visual_C) (C++_Primer) –> (Perl_for_dummies,Tcl_Tk)
Athletic Apparel Store:
(Shoes) (Racket, Racketball) –> (Sports_Jacket)
Regression
Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.
Greatly studied in statistics, neural network fields.
Examples:
Predicting sales amounts of new product based on advetising expenditure.
Predicting wind velocities as a function of temperature, humidity, air pressure, etc.
Time series prediction of stock market indices.
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
Challenges of Data Mining
Scalability
Dimensionality
Complex and Heterogeneous Data
Data Quality
Data Ownership and Distribution
Privacy Preservation
Streaming Data
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
4,000,000
19951996199719981999
Tid
Refund
Marital
Status
Taxable
Income
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
Yes
10
Refund
Marital
Status
Taxable
Income
Cheat
No
Single
75K
?
Yes
Married
50K
?
No
Married
150K
?
Yes
Divorced
90K
?
No
Single
40K
?
No
Married
80K
?
10
Category
Total
Articles
Correctly
Placed
Financial
555
364
Foreign
341
260
National
273
36
Metro
943
746
Sports
738
573
Entertainment
354
278
Discovered Clusters
Industry Group
1
Applied-Matl-DOWN,Bay-Network-Down,3-COM-DOWN,
Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN,
DSC-Comm-DOWN,INTEL-DOWN,LSI-Logic-DOWN,
Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down,
Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOWN,
Sun-DOWN
Technology1-DOWN
2
Apple-Comp-
DOWN,Autodesk-DOWN,DEC-DOWN,
ADV-Micro-Device-
DOWN,Andrew-Corp-DOWN,
Computer-Assoc-DOWN,Circuit-City-DOWN,
Compaq-DOWN, EMC-Corp-DOWN,
Gen-Inst-DOWN,
Motorola-DOWN,Microsoft-DOWN,Scientific-Atl-DOWN
Technology2-DOWN
3
Fannie-Mae-
DOWN,Fed-Home-Loan-DOWN,
MBNA-Corp-
DOWN,Morgan-Stanley-DOWN
Financial-DOWN
4
Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP,
Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP,
Schlumberger-UP
Oil-UP
TID
Items
1
Bread, Coke, Milk
2
Beer, Bread
3
Beer, Coke, Diaper, Milk
4
Beer, Bread, Diaper, Milk
5
Coke, Diaper, Milk
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