CAP 4770:
Introduction to Data Mining
Fall 2010
Dr. Tao Li
Florida International University
Self-Introduction
• Ph.D. from University of Rochester, 2004
• Associate Professor in the School of Computer
Science at Florida International University
• Research Interest
–
–
–
–
Data Mining
Machine Learning
Information Retrieval
Bioinformatics
• Industry Experience:
– Summer internships at Xerox Research (summer
2001, 2002) and IBM Research (Summer 2003, 2004)
CAP 4770
2
My Research Projects
• You can find on
http://www.cis.fiu.edu/~taoli
CAP 4770
3
Student Self-Introduction
• Name
– I will try to remember your names. But if you
have a Long name, please let me know how
should I call you 
• Major and Academic status
• Programming Skills
– Java, C/C++, VB, Matlab, Scripts etc.
• Anything you want us to know
– e.g., I am a spurs fan. 
CAP 4770
4
Acknowledgements
• Some of the material used in this
course is drawn from other sources:
• Prof. Christopher W. Clifton at Purdue
• Prof. Jiawei Han at UIUC
• Profs. Pang-Ning Tan (Michigan State
University), Michael Steinbach and
Vipin Kumar (University of Minnesota)
CAP 4770
5
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
– Curse of Dimensionality
CAP 4770
6
Course Overview
• Meeting time
– T/Th 2:00pm – 3:15pm
• Office hours:
– Tuesday 5:00pm – 6:00pm or by appointment
• Course Webpage:
– http://www.cs.fiu.edu/~taoli/class/CAP4770F10/index.html
– Lecture Notes and Assignments
CAP 4770
7
Course Objectives
This is an introductory course for junior/senior
computer science undergraduate students
on the topic of Data Mining. Topics include
data mining applications, data preparation,
data reduction and various data mining
techniques (such as association, clustering,
classification, anomaly detection)
CAP 4770
8
Assignments and Grading
•
•
•
•
•
•
Reading/Written Assignments
Research Projects
Midterm Exams
Final Project/Presentations
Class attendance is mandatory.
Evaluation will be a subjective process
– Effort is very important component
•
•
•
•
Class Participation: 10%
Quizzes: 10%
Exams: 30%
Assignments: 50%
– Final Project: 15%
– Written Homework: 15%
– Other Projects: 20%
CAP 4770
9
Text and References
• Jiawei Han and Micheline Kamber. Data
Mining: Concepts and Techniques.
• Ian H. Witten and Eibe Frank. Data
Mining: Practical Machine Learning
Tools and Techniques with Java
Implementations.
CAP 4770
10
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
– Curse of Dimensionality
CAP 4770
11
Why Data Mining?
• Motivation: “Necessity is the Mother of Invention”
• Data explosion problem
– Applications generate huge amounts of data
• WWW, computer systems/programs, biology experiments, Business
transactions, Scientific computation and simulation, Medical and person
data, Surveillance video and pictures, Satellite sensing, Digital media,
– Technologies are available to collect and store data
• Bar codes, scanners, satellites, cameras etc.
• Databases, data warehouses, variety of repositories …
– We are drowning in data, but starving for knowledge!
CAP 4770
12
What Is Data Mining?
• Data mining (knowledge discovery from data)
– Extraction of interesting (non-trivial, implicit, previously unknown and
potentially useful) patterns or knowledge from huge amount of data
• What is not data mining?
– (Deductive) query processing.
– Expert systems or small ML/statistical programs
• Key Characteristics
– Combination of Theory and Application
– Engineering Process
• Data Pre-processing and Post-processing, Interpretation
– Collection of Functionalities
• Different Tasks and Algorithms
– Interdisciplinary Field
CAP 4770
13
Real Example from NBA
• AS (Advanced Scout) software from IBM
Research
– Coach can assess the effectiveness of certain
coaching decisions
• Good/bad player matchups
• Plays that work well against a given team
• Raw Data: Play-by-play information recorded by
teams
– Who is on court
– Who took a shot, the type of shot, the outcome, any
rebounds
CAP 4770
14
AS Knowledge Discovery
• Text Description
– When Price was Point-Guard, J. Williams
made 100% of his jump field-goal-attempts.
The total number of such attempts is 4.
• Graph Description
Starks+Houston+
Ward playing
Shooting
Percentage
Overall
0
20
40
60
Reference:
Bhabdari et al. Advanced Scout: Data Mining and Knowledge Discovery in NBA
Data. Data Mining and Knowledge Discovery, 1, 121-125(1997)
CAP 4770
15
Ads vs. search results
Ads vs. search results
• Search advertising is the revenue model
– Multi-billion-dollar industry
– Advertisers pay for clicks on their ads
• Interesting problems
– What ads to show for a search?
– If I’m an advertiser, which search terms
should I bid on and how much to bid?
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
– Curse of Dimensionality
CAP 4770
19
Potential Applications
• Data analysis and decision support
– Market analysis and management
• Target marketing, customer relationship management (CRM), market
basket analysis, cross selling, market segmentation
– Risk analysis and management
• Forecasting, customer retention, improved underwriting, quality control,
competitive analysis
– Fraud detection and detection of unusual patterns (outliers)
• Other Applications
–
–
–
–
Text mining (news group, email, documents) and Web mining
Stream data mining
System and Network Management
Multimedia Applications
• Music, Image, Video
– DNA and bio-data analysis
CAP 4770
20
Example: Use in retailing
• Goal: Improved business efficiency
– Improve marketing (advertise to the most likely buyers)
– Inventory reduction (stock only needed quantities)
• Information source: Historical business data
– Example: Supermarket sales records
Date/Time/Register
12/6 13:15 2
12/6 13:16 3
Fish
N
Y
Turkey
Y
N
Cranberries
Y
N
Wine
N
Y
...
...
...
– Size ranges from 50k records (research studies) to terabytes
(years of data from chains)
– Data is already being warehoused
• Sample question – what products are generally
purchased together?
• The answers are in the data, if only we could see them
CAP 4770
21
Other Applications
• Network System management
– Event Mining Research at IBM
• Astronomy
– JPL and the Palomar Observatory discovered 22
quasars with the help of data mining
• Internet Web Surf-Aid
– IBM Surf-Aid applies data mining algorithms to Web
access logs for market-related pages to discover
customer preference and behavior pages, analyzing
effectiveness of Web marketing, improving Web site
organization, etc.
CAP 4770
22
Market Analysis and Management (1)
• Where are the data sources for analysis?
– Credit card transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
• Target marketing
– Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.
• Determine customer purchasing patterns over time
– Conversion of single to a joint bank account: marriage, etc.
• Cross-market analysis
– Associations/co-relations between product sales
– Prediction based on the association information
CAP 4770
23
Market Analysis and Management (2)
• Customer profiling
– data mining can tell you what types of customers buy what
products (clustering or classification)
• Identifying customer requirements
– identifying the best products for different customers
– use prediction to find what factors will attract new customers
• Provides summary information
– various multidimensional summary reports
– statistical summary information (data central tendency and
variation)
CAP 4770
24
Corporate Analysis and Risk
Management
• Finance planning and asset evaluation
– cash flow analysis and prediction
– contingent claim analysis to evaluate assets
– cross-sectional and time series analysis (financialratio, trend analysis, etc.)
• Resource planning:
– summarize and compare the resources and spending
• Competition:
– monitor competitors and market directions
– group customers into classes and a class-based
pricing procedure
– set pricing strategy in a highly competitive market
CAP 4770
25
Fraud Detection and Management (1)
• Applications
– widely used in health care, retail, credit card services,
telecommunications (phone card fraud), etc.
• Approach
– use historical data to build models of fraudulent behavior and
use data mining to help identify similar instances
• Examples
– auto insurance: detect a group of people who stage accidents to
collect on insurance
– money laundering: detect suspicious money transactions (US
Treasury's Financial Crimes Enforcement Network)
– medical insurance: detect professional patients and ring of
doctors and ring of references
CAP 4770
26
Fraud Detection and Management (2)
• Detecting inappropriate medical treatment
– Australian Health Insurance Commission identifies
that in many cases blanket screening tests were
requested (save Australian $1m/yr).
• Detecting telephone fraud
– Telephone call model: destination of the call, duration,
time of day or week. Analyze patterns that deviate
from an expected norm.
– British Telecom identified discrete groups of callers
with frequent intra-group calls, especially mobile
phones, and broke a multimillion dollar fraud.
• Retail
– Analysts estimate that 38% of retail shrink is due to
dishonest employees.
CAP 4770
27
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
– Curse of Dimensionality
CAP 4770
28
Data Mining: An Engineering
Process
– Data mining: interactive
and iterative process.
Interpretation/
Evaluation
Mining
Algorithms
Knowledge
Preprocessing
Patterns
Selection
Preprocessed
Data
Data
Target
Data
adapted from:
U. Fayyad, et al. (1995), “From Knowledge Discovery to Data
Mining: An Overview,” Advances in Knowledge Discovery and
Data Mining, U. Fayyad et al. (Eds.), AAAI/MIT Press
CAP 4770
29
Steps of a KDD Process
• Learning the application domain
– relevant prior knowledge and goals of application
• Creating a target data set: data selection
• Data cleaning and preprocessing: (may take 60% of effort!)
• Data reduction and transformation
– Find useful features, dimensionality/variable reduction, invariant
representation.
• Choosing functions of data mining
– summarization, classification, regression, association, clustering.
• Choosing the mining algorithm(s)
• Data mining: search for patterns of interest
• Pattern evaluation and knowledge presentation
– visualization, transformation, removing redundant patterns, etc.
• Use of discovered knowledge
CAP 4770
30
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
– Curse of Dimensionality
CAP 4770
31
Architecture of a Typical Data
Mining System
Graphical user interface
Pattern evaluation
Data mining engine
Knowledge-base
Database or data
warehouse server
Data cleaning & data integration
Filtering
Data
Warehouse
Databases
CAP 4770
32
Data Mining: On What Kind
of Data?
•
•
•
•
Relational databases
Data warehouses
Transactional databases
Advanced DB and information repositories
–
–
–
–
–
–
Object-oriented and object-relational databases
Spatial databases
Time-series data and temporal data
Text databases and multimedia databases
Heterogeneous and legacy databases
WWW
CAP 4770
33
What Can Data Mining Do?
• Cluster
• Classify
– Categorical, Regression
• Semi-supervised
• Summarize
– Summary statistics, Summary rules
• Link Analysis / Model Dependencies
– Association rules
• Sequence analysis
– Time-series analysis, Sequential associations
• Detect Deviations
CAP 4770
34
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
CAP 4770
35
Data Mining Tasks...
•
•
•
•
•
•
Classification [Predictive]
Clustering [Descriptive]
Association Rule Discovery [Descriptive]
Sequential Pattern Discovery [Descriptive]
Regression [Predictive]
Deviation Detection [Predictive]
CAP 4770
36
Classification Example
T id
R e fu n d
R e fu n d
M a rita l
S ta tu s
T a x a b le
In c o m e
C heat
M a rita l
S ta tu s
T a x a b le
In c o m e
C heat
1
Yes
S in g le
125K
No
No
S in g le
75K
?
2
No
M a rrie d
100K
No
Yes
M a rrie d
50K
?
3
No
S in g le
70K
No
No
M a rrie d
150K
?
4
Yes
M a rrie d
120K
No
Yes
D ivo rc e d
90K
?
5
No
D ivo rc e d
95K
Yes
No
S in g le
40K
?
6
No
M a rrie d
60K
No
No
M a rrie d
80K
?
10
7
Yes
D ivo rc e d
220K
No
8
No
S in g le
85K
Yes
9
No
M a rrie d
75K
No
10
10
No
S in g le
90K
Yes
Training
Set
CAP 4770
Learn
Classifier
Test
Set
Model
37
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.
CAP 4770
38
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 information as input attributes to learn a classifier
model.
From [Berry & Linoff] Data Mining Techniques, 1997
CAP 4770
39
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.
CAP 4770
40
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 timeof-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
CAP 4770
41
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
CAP 4770
42
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.
CAP 4770
43
Illustrating Clustering
Euclidean Distance Based Clustering in 3-D space.
Intracluster distances
are minimized
Intercluster distances
are maximized
CAP 4770
44
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.
CAP 4770
45
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
CAP 4770
46
term to clustered documents.
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.
1
2
3
4
Di scov ered Cl u sters
In du stry G rou p
A p p lie d -M a tl- D O W N , Ba y -N e t w o rk - D o w n , 3- C O M -D O W N ,
Ca b le tro n -Sy s -D O W N ,C IS C O - D O W N , H P -D O W N ,
D S C- Co m m - D O W N ,I N T E L - D O W N , LS I- Lo g ic -D O W N ,
M ic ro n -T e c h -D O W N ,T e xa s -In s t-D o w n ,T e lla b s -In c -D o w n ,
N a tl- Se m ic o n d u c t-D O W N , O ra c l- D O W N , S G I- D O W N ,
Su n -D O W N
Tec hno lo gy1- D O W N
A p p le -Co mp - D O W N ,A u to d es k-D O W N , D E C- D O W N ,
A D V-M ic ro - D e v ic e -D O W N ,A n d re w - Co rp - D O W N ,
Co mp u te r-A s so c -D O W N , Circ u it- C ity -D O W N ,
Co mp a q -D O W N , EM C- Co rp - D O W N , G e n - In s t-D O W N ,
M o to ro la -D O W N ,M ic ro s o ft-D O W N ,Sc ie n tif ic -A tl-D O W N
Fa n n ie -M a e - D O W N ,Fe d - H o me - Lo a n - D O W N ,
M BN A - Co rp -D O W N ,M o rg a n - Sta n le y -D O W N
Ba ke r- H u g h e s -U P, D re s s e r-In d s -U P, H a llib u rto n -H LD -U P,
Lo u is ia n a - La n d - U P, Ph illip s -Pe tro - U P, U n o c a l- U P,
Sc h lu mb e rg e r- U P
CAP 4770
Tec hno lo gy2- D O W N
F ina nc ia l- D O W N
O il- U P
47
Association Rule Discovery:
Definition
• Given a set of records each of which contain
some number of items from a given collection;
T ID
– Produce dependency rules which will predict
occurrence of an item based on occurrences of other
Item
s
items.
1
B rea d , C o k e, M ilk
2
B eer, B rea d
{Milk} --> {Coke}
3
B eer, C o k e, D ia p er, M ilk
{Diaper, Milk} --> {Beer}
4
B eer, B rea d , D ia p er, M ilk
5
C o k e, D ia p er, M ilk
Rules Discovered:
CAP 4770
48
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!
CAP 4770
49
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! CAP 4770
50
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.
CAP 4770
51
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)
<= xg
(C)
(D E)
>ng
<= ws
<= ms
CAP 4770
52
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)
CAP 4770
53
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.
CAP 4770
54
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
CAP 4770
55
Are All the “Discovered” Patterns
Interesting?
• A data mining system/query may generate thousands of
patterns, not all of them are interesting.
– Suggested approach: Human-centered, query-based, focused
mining
• Interestingness measures: A pattern is interesting if it is
easily understood by humans, valid on new or test data
with some degree of certainty, potentially useful, novel, or
validates some hypothesis that a user seeks to confirm
– Objective vs. subjective interestingness measures:
CAP 4770
57
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
CAP 4770
58
Multiple Disciplines
Artificial
Intelligence
Machine
Learning
Database
Management
Statistics
Visualization
Algorithms
Data
Mining
Information
Retrieval
Systems
CAP 4770
59
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
CAP 4770
60
Data Mining: Classification Schemes
• General functionality
– Descriptive data mining
– Predictive data mining
• Different views, different classifications
– Kinds of databases to be mined
– Kinds of knowledge to be discovered
– Kinds of techniques utilized
– Kinds of applications adapted
CAP 4770
61
Multi-Dimensional View of
Data Mining
• Data to be mined
– Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media,
heterogeneous, legacy, WWW
• Knowledge to be mined
– Characterization, discrimination, association, classification,
clustering, trend/deviation, outlier analysis, etc.
– Multiple/integrated functions and mining at multiple levels
• Techniques utilized
– Database-oriented, data warehouse (OLAP), machine learning,
statistics, visualization, etc.
• Applications adapted
– Retail, telecommunication, banking, fraud analysis, bio-data
mining, stock market analysis, Web mining, etc.
CAP 4770
62
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
CAP 4770
63
History of the Data Mining
• Knowledge Discovery in Databases workshops started
‘89
– Now a conference under the auspices of ACM SIGKDD
– IEEE conference series started 2001
• Key founders / technology contributors:
– Usama Fayyad, JPL (then Microsoft, now has his own company,
Digimine)
– Gregory Piatetsky-Shapiro (then GTE, now his own data mining
consulting company, Knowledge Stream Partners)
– Rakesh Agrawal (IBM Research)
The term “data mining” has been around since at least
1983 – as a pejorative term in the statistics community
CAP 4770
64
Outline
• Course Logistics
• Data Mining Introduction
• Four Key Characteristics
–
–
–
–
Combination of Theory and Application
Engineering Process
Collection of Functionalities
Interdisciplinary field
• How do we categorize data mining systems?
• History of Data Mining
• Research Issues
CAP 4770
66
Data Mining Complications
• Volume of Data
– Clever algorithms needed for reasonable performance
• Interest measures
– How do we ensure algorithms select “interesting” results?
• “Knowledge Discovery Process” skill required
– How to select tool, prepare data?
• Data Quality
– How do we interpret results in light of low quality data?
• Data Source Heterogeneity
– How do we combine data from multiple sources?
CAP 4770
67
Research Issues
•
Mining methodology
– Mining different kinds of knowledge from diverse data types, e.g., bio, stream,
Web
– Performance: efficiency, effectiveness, and scalability
– Pattern evaluation: the interestingness problem
– Incorporation of background knowledge
– Handling noise and incomplete data
– Parallel, distributed and incremental mining methods
– Integration of the discovered knowledge with existing one: knowledge fusion
•
User interaction
– Data mining query languages and ad-hoc mining
– Expression and visualization of data mining results
– Interactive mining of knowledge at multiple levels of abstraction
•
Applications and social impacts
– Domain-specific data mining & invisible data mining
– Protection of data security, integrity, and privacy
CAP 4770
68
Descargar

Slides for COP5992 - Florida International University