Transcript
An Introduction to Data Mining
BY:GAGAN DEEP KAUSHAL
Trends leading to Data Flood More data is generated: Bank, telecom, other business transactions ... Scientific Data: astronomy, biology, etc Web, text, and e-commerce
More data is captured: Storage technology faster and cheaper DBMS capable of handling COM 307: Machine Learning and Data Mining bigger DB
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Growth Trends Moore’s law Computer Speed doubles every 18 months
Storage law total storage doubles every 9 months
Consequence very little data will ever be looked at by a human
Data Mining is NEEDED to make sense and use of data. COM 307: Machine Learning and Data Mining
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Data Mining Definition
Data mining in Data is the
non-trivial process of identifying valid novel potentially useful and ultimately understandable patterns in data. COM 307: Machine Learning and Data Mining
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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 not Data Mining? ●
– Look up phone number in phone directory – Query a Web search engine for information about “Amazon”
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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,)
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 ●
Computers have become cheaper and more powerful
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Competitive Pressure is Strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management)
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
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Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data
Related Fields Machine Learning
Visualization
Data Mining and Knowledge Discovery
Statistics
Databases
COM 307: Machine Learning and Data Mining
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Data Mining Process Integration
Da
ta
Se & lect Cl io ea n nin g
DATA Ware house
an sfo
rm ati on
Target Data
Mi
Knowledge
nin
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Transformed Data
Knowledge
Patterns and Rules
COM 307: Machine Learning and Data Mining
Understanding
R awD ata
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Interpretation & Evaluation
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Major Data Mining Tasks Classification: predicting an item class Associations: e.g. A & B & C occur frequently Visualization: to facilitate human discovery Estimation: predicting a continuous value Deviation Detection: finding changes Link Analysis: finding relationships … COM 307: Machine Learning and Data Mining
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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.
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 al al us c c i i o or or nu i g g t te te n ss a a o a l c c c c
Refund
Marital Status
Taxable Income Cheat
No
No
Single
75K
?
100K
No
Yes
Married
50K
?
Single
70K
No
No
Married
150K
?
Yes
Married
120K
No
Yes
Divorced 90K
?
5
No
Divorced
95K
Yes
No
Single
40K
?
6
No
Married
60K
No
No
Married
80K
?
Tid
Refund
Marital Status
Taxable Income
Cheat
1
Yes
Single
125K
2
No
Married
3
No
4
10
10
7
Yes
Divorced
220K
No
8
No
Single
85K
Yes
9
No
Married
75K
No
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 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 accountholder 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.
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. TID
Items
1 2 3 4 5
Bread, Coke, Milk Beer, Bread Beer, Coke, Diaper, Milk Beer, Bread, Diaper, Milk Coke, Diaper, Milk
Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer}
Association Rule Discovery: Application 1 ●
Marketing and Sales Promotion: – Let the rule discovered be {Patty, … } --> {Potato Chips} – Potato Chips as consequent => Can be used to determine what should be done to boost its sales. – Patties in the antecedent => Can be used to see which products would be affected if the store discontinues selling patties. – Patty in antecedent and Potato chips in consequent => Can be used to see what products should be sold with patty to promote sale of Potato chips!
Association Rule Discovery: Application 1 ●
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.
Association Rule Discovery: Application 2 ●
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.
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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.
(A B) ●
(C)
(D E)
Rules are formed by first disovering patterns. Event occurrences in the patterns are governed by timing constraints.
(A B)
(C)
(D E)
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Sequential Pattern Discovery: Examples In telecommunications alarm logs, – (Inverter_Problem Excessive_Line_Current) (Rectifier_Alarm) --> (Fire_Alarm)
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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 ●
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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 ●
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