Mining Frequent Patterns Without Candidate Generation

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Transcript Mining Frequent Patterns Without Candidate Generation

Data Mining:
Concepts and Techniques
July 21, 2015
Data Mining: Concepts and Techniques
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Chapter 1. Introduction
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Motivation: Why data mining?
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What is data mining?
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Data Mining: On what kind of data?
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Data mining functionality
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Are all the patterns interesting?
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Major issues in data mining
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Data Mining: Concepts and Techniques
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Motivation: “Necessity is the
Mother of Invention”
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Data explosion problem
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Automated data collection tools and mature database technology
lead to tremendous amounts of data stored in databases, data
warehouses and other information repositories
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We are drowning in data, but starving for knowledge!
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Solution: Data warehousing and data mining
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Data warehousing and on-line analytical processing
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Extraction of interesting knowledge (rules, regularities, patterns,
constraints) from data in large databases
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Evolution of Database Technology
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1960s:
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1970s:
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Relational data model, relational DBMS implementation
1980s:
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Data collection, database creation, IMS and network DBMS
RDBMS, advanced data models (extended-relational, OO,
deductive, etc.) and application-oriented DBMS (spatial, scientific,
engineering, etc.)
1990s—2000s:
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Data mining and data warehousing, multimedia databases, and
Web databases
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What Is Data Mining?
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Data mining (knowledge discovery in databases):
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Alternative names:
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Data mining: a misnomer?
Knowledge discovery(mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting,
business intelligence, etc.
What is not data mining?
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Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) information or patterns
from data in large databases
(Deductive) query processing.
Expert systems or small ML/statistical programs
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Why Data Mining? — Potential
Applications
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Database analysis and decision support
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Market analysis and management
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Risk analysis and management
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target marketing, customer relation management, market
basket analysis, cross selling, market segmentation
Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
Fraud detection and management
Other Applications
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Text mining (news group, email, documents)
Stream data mining
Web mining.
DNA data analysis
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Market Analysis and Management (1)
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Where are the data sources for analysis?
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Target marketing
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Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.
Determine customer purchasing patterns over time
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Credit card transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
Conversion of single to a joint bank account: marriage, etc.
Cross-market analysis
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Associations/co-relations between product sales
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Prediction based on the association information
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Market Analysis and Management (2)
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Customer profiling
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data mining can tell you what types of customers buy what
products (clustering or classification)
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Identifying customer requirements
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identifying the best products for different customers
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use prediction to find what factors will attract new customers
Provides summary information
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various multidimensional summary reports
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statistical summary information (data central tendency and
variation)
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Corporate Analysis and Risk
Management
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Finance planning and asset evaluation
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Resource planning:
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cash flow analysis and prediction
contingent claim analysis to evaluate assets
cross-sectional and time series analysis (financial-ratio, trend
analysis, etc.)
summarize and compare the resources and spending
Competition:
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monitor competitors and market directions
group customers into classes and a class-based pricing
procedure
set pricing strategy in a highly competitive market
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Fraud Detection and Management (1)
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Applications
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Approach
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widely used in health care, retail, credit card services,
telecommunications (phone card fraud), etc.
use historical data to build models of fraudulent behavior and
use data mining to help identify similar instances
Examples
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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
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Fraud Detection and Management (2)
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Detecting inappropriate medical treatment
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Detecting telephone fraud
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Australian Health Insurance Commission identifies that in many
cases blanket screening tests were requested (save Australian
$1m/yr).
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
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Analysts estimate that 38% of retail shrink is due to dishonest
employees.
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Other Applications
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Sports
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Astronomy
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IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for
New York Knicks and Miami Heat
JPL and the Palomar Observatory discovered 22 quasars with
the help of data mining
Internet Web Surf-Aid
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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.
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Data Mining: A KDD Process
Pattern Evaluation
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Data mining: the core of
knowledge discovery
Data Mining
process.
Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases
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Steps of a KDD Process
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Learning the application domain:
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Creating a target data set: data selection
Data cleaning and preprocessing: (may take 60% of effort!)
Data reduction and transformation:
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summarization, classification, regression, association, clustering.
Choosing the mining algorithm(s)
Data mining: search for patterns of interest
Pattern evaluation and knowledge presentation
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Find useful features, dimensionality/variable reduction, invariant
representation.
Choosing functions of data mining
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relevant prior knowledge and goals of application
visualization, transformation, removing redundant patterns, etc.
Use of discovered knowledge
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Data Mining and Business Intelligence
Increasing potential
to support
business decisions
Making
Decisions
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
End User
Business
Analyst
Data
Analyst
Data Exploration
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
OLAP, MDA
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP
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Data Mining: Concepts and Techniques
DBA
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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
Databases
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Filtering
Data
Warehouse
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Data Mining: On What Kind of
Data?
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Relational databases
Data warehouses
Transactional databases
Advanced DB and information repositories
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Object-oriented and object-relational databases
Spatial and temporal data
Time-series data and stream data
Text databases and multimedia databases
Heterogeneous and legacy databases
WWW
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Data Mining Functionalities
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Concept description:
Characterization and discrimination
Name
Gender
Jim
Woodman
Initial
Scott
Relation Lachance
Laura Lee
…
Removed
M
Major
Residence
Phone #
GPA
M
F
…
Retained
M
F
…
Birth_date
Vancouver,BC, 8-12-76
Canada
CS
Montreal, Que, 28-7-75
Canada
Physics Seattle, WA, USA 25-8-70
…
…
…
3511 Main St.,
Richmond
345 1st Ave.,
Richmond
687-4598
3.67
253-9106
3.70
125 Austin Ave.,
Burnaby
…
420-5232
…
3.83
…
Sci,Eng,
Bus
City
Removed
Excl,
VG,..
CS
Gender Major
Prime
Generalized
Relation
Birth-Place
Science
Science
…
Country
Age range
Birth_region
Age_range
Residence
GPA
Canada
Foreign
…
20-25
25-30
…
Richmond
Burnaby
…
Very-good
Excellent
…
Birth_Region
Canada
Foreign
Total
Gender
M
16
14
30
F
10
22
32
Total
26
36
62
Count
16
22
…
Association Rule Mining
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Association rule mining:
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Finding frequent patterns, associations, correlations, or causal
structures among sets of items or objects in transaction
databases, relational databases, and other information
repositories.
Frequent pattern: pattern (set of items, sequence, etc.) that
occurs frequently in a database
Motivation: finding regularities in data
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What products were often purchased together? — Beer and
diapers?!
What are the subsequent purchases after buying a PC?
What kinds of DNA are sensitive to this new drug?
Can we automatically classify web documents?
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Association Rule Mining (cont.)
Transaction-id
10
20
30
40
Customer
buys both
Customer
buys beer
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Items bought
A, B, C
A, C
A, D
B, E, F
Customer
buys
diapers
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Itemset X={x1, …, xk}
Find all the rules XY with min
confidence and support
 support, s, probability that a
transaction contains XY
 confidence, c, conditional
probability that a transaction
having X also contains Y.
Let min_support = 50%,
min_conf = 50%:
A  C (50%, 66.7%)
C  A (50%, 100%)
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Visualization of Association Rules
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Classification and Prediction
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Finding models (functions) that describe and
distinguish classes or concepts for future prediction
E.g., classify countries based on climate, or classify
cars based on gas mileage
Presentation: decision-tree, classification rule, neural
network
Prediction: Predict some unknown or missing
numerical values
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Classification Process: Model
Construction
Classification
Algorithms
Training
Data
NAME RANK
M ike
M ary
B ill
Jim
D ave
Anne
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A ssistan t P ro f
A ssistan t P ro f
P ro fesso r
A sso ciate P ro f
A ssistan t P ro f
A sso ciate P ro f
Classifier
(Model)
YEARS TENURED
3
7
2
7
6
3
no
yes
yes
yes
no
no
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’
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Classification Process: Use the
Model in Prediction
Classifier
Testing
Data
Unseen Data
(Jeff, Professor, 4)
NAME
Tom
M erlisa
G eorge
Joseph
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RANK
Y E A R S TE N U R E D
A ssistant P rof
2
no
A ssociate P rof
7
no
P rofessor
5
yes
A ssistant P rof
7
yes
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Tenured?
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Decision Trees
Training
set
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age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income
high
high
high
medium
low
low
low
medium
low
medium
medium
medium
high
medium
student
no
no
no
no
yes
yes
yes
no
yes
yes
yes
no
yes
no
Data Mining: Concepts and Techniques
credit_rating
fair
excellent
fair
fair
fair
excellent
excellent
fair
fair
fair
excellent
excellent
fair
excellent
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Output: A Decision Tree for
“buys_computer”
age?
<=30
student?
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overcast
30..40
yes
>40
credit rating?
no
yes
excellent
fair
no
yes
no
yes
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Presentation of Classification Results
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Cluster and outlier analysis
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Cluster analysis
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Class label is unknown: Group data to form new classes, e.g., cluster
houses to find distribution patterns
Clustering based on the principle: maximizing the intra-class similarity
and minimizing the interclass similarity
Outlier analysis
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Outlier: a data object that does not comply with the general behavior of
the data
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It can be considered as noise or exception but is quite useful in fraud
detection, rare events analysis
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Clusters and Outliers
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Non-Traditional Mining Techniques
Web Mining
Web Mining
Web Content
Mining
 Text-based search
Web Structure
Mining
Web Usage
Mining
 Google
engines
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Are All the “Discovered” Patterns
Interesting?
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A data mining system/query may generate thousands of patterns,
not all of them are interesting.
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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
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Objective vs. subjective interestingness measures:
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Objective: based on statistics and structures of patterns, e.g., support,
confidence, etc.
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Subjective: based on user’s belief in the data, e.g., unexpectedness,
novelty, actionability, etc.
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Data Mining: Confluence of Multiple
Disciplines
Database
Technology
Machine
Learning
Information
Science
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Statistics
Data Mining
Visualization
Other
Disciplines
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Major Issues in Data Mining (1)
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Mining methodology and user interaction
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Mining different kinds of knowledge in databases
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Interactive mining of knowledge at multiple levels of abstraction
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Incorporation of background knowledge
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Data mining query languages and ad-hoc data mining
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Expression and visualization of data mining results
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Handling noise and incomplete data
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Pattern evaluation: the interestingness problem
Performance and scalability
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Efficiency and scalability of data mining algorithms
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Parallel, distributed and incremental mining methods
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Major Issues in Data Mining (2)
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Issues relating to the diversity of data types
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Handling relational and complex types of data
Mining information from heterogeneous databases and global
information systems (WWW)
Issues related to applications and social impacts
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Application of discovered knowledge
 Domain-specific data mining tools
 Intelligent query answering
 Process control and decision making
Integration of the discovered knowledge with existing knowledge:
A knowledge fusion problem
Protection of data security, integrity, and privacy
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Summary
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Data mining: discovering interesting patterns from large amounts of
data
A natural evolution of database technology, in great demand, with
wide applications
A KDD process includes data cleaning, data integration, data
selection, transformation, data mining, pattern evaluation, and
knowledge presentation
Mining can be performed in a variety of information repositories
Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
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Classification of data mining systems
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Major issues in data mining
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A Brief History of Data Mining
Society
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1989 IJCAI Workshop on Knowledge Discovery in Databases
(Piatetsky-Shapiro)
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1991-1994 Workshops on Knowledge Discovery in Databases
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Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, 1996)
1995-1998 International Conferences on Knowledge Discovery in
Databases and Data Mining (KDD’95-98)
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Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
Journal of Data Mining and Knowledge Discovery (1997)
1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD
Explorations
More conferences on data mining
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PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc.
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