Download Mining Frequent Patterns Without Candidate Generation

Data Mining:
Concepts and Techniques
— Chapter 1 —
Slides adopted from the book resources
October 10, 2006
Data Mining: Concepts and Techniques
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Data Mining: Concepts and Techniques
October 10, 2006
Data Mining: Concepts and Techniques
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References
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Main reference:
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Data Mining Concepts and Techniques, Han and Kamber
Supplementary:
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Daniel T. Larose, Discovering knowledge in Data: An Introduction
to Data Mining, Wiley, 2005.
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D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining,
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MIT Press, 2001.
Witten, Ian and Eibe Frank, Data Mining, Practical Machine
Learning Tools and Techniques with Java Implementations,
Morgan Kaufmann, 1999.
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KDnuggets.com: News, Publications, Software, Solutions, …
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Many papers and web resources, some will be posted on course
web site.
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Data Mining: Concepts and Techniques
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Tentative schedule
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Data Mining: Concepts and Techniques
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Tentative schedule (cont.)
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Data Mining: Concepts and Techniques
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Grading policy
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Midterm: 4-6 points
Final exam: 7-9 points
Home-works: 2-4 points
Projects: 2-4 points
Research paper: 1-3 points
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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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Classification of data mining systems
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Major issues in data mining
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Data Mining: Concepts and Techniques
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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 accumulated and/or to be
analyzed 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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Miing interesting knowledge (rules, regularities, patterns,
constraints) from data in large databases
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Data Mining: Concepts and Techniques
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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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RDBMS, advanced data models (extended-relational, OO, deductive, etc.)
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Application-oriented DBMS (spatial, scientific, engineering, etc.)
1990s:
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Data collection, database creation
Data mining, data warehousing, multimedia databases, and Web
databases
2000s
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Stream data management and mining
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Data mining with a variety of applications
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Web technology and global information systems
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Data Mining: Concepts and Techniques
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What Is Data Mining?
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Data mining (knowledge discovery from data)
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Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) patterns or knowledge from
huge amount of data
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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.
Watch out: Is everything “data mining”?
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(Deductive) query processing.
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Expert systems or small ML/statistical programs
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Data Mining: Concepts and Techniques
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Why Data Mining?—Potential Applications
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Data analysis and decision support
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Market analysis and management
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Target marketing, customer relationship management (CRM),
market basket analysis, cross selling, market segmentation
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Risk analysis and management
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Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
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Fraud detection and detection of unusual patterns (outliers)
Other Applications
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Text mining (news group, email, documents) and Web mining
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Stream data mining
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DNA and bio-data analysis
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Market Analysis and Management
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Where does the data come from?
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Target marketing
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Determine customer purchasing patterns over time
Associations/co-relations between product sales, & prediction based on such association
Customer profiling
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Find clusters of “model” customers who share the same characteristics: interest, income level,
spending habits, etc.
Cross-market analysis
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Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus
(public) lifestyle studies
What types of customers buy what products (clustering or classification)
Customer requirement analysis
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identifying the best products for different customers
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predict what factors will attract new customers
Provision of summary information
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multidimensional summary reports
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statistical summary information (data central tendency and variation)
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Corporate Analysis & 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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Data Mining: Concepts and Techniques
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Fraud Detection & Mining Unusual Patterns
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Approaches: Clustering & model construction for frauds, outlier analysis
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Applications: Health care, retail, credit card service, telecomm.
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Auto insurance: ring of collisions
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Money laundering: suspicious monetary transactions
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Medical insurance
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Professional patients, ring of doctors, and ring of references
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Unnecessary or correlated screening tests
Telecommunications: phone-call fraud
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Retail industry
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Phone call model: destination of the call, duration, time of day or
week. Analyze patterns that deviate from an expected norm
Analysts estimate that 38% of retail shrink is due to dishonest
employees
Anti-terrorism
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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.
Data Mining: Concepts and Techniques
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Data Mining: A KDD Process
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Data mining—core of
knowledge discovery
process
Pattern Evaluation
Data Mining
Task-relevant Data
Selection
Data Warehouse
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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relevant prior knowledge and goals of application
Find useful features, dimensionality/variable reduction, invariant
representation.
Choosing functions of data mining
summarization, classification, regression, association, clustering.
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Choosing the mining algorithm(s)
Data mining: search for patterns of interest
Pattern evaluation and knowledge presentation
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visualization, transformation, removing redundant patterns, etc.
Use of discovered knowledge
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The Need for Human Direction of Data
Mining
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Some early data mining definitions described process as “automatic”
“…this has misled many people into believing data mining is product
that can be bought rather than a discipline that must be mastered.”
(Berry, Linoff)
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Automation no substitute for human input
Data mining is easy to do badly
Understanding statistical and mathematical model structures of
underlying software required
Humans need to be actively involved in every phase of data mining
process
Task of data mining should be integrated into human process of
problem solving
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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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Cross Industry Standard Process:
CRISP-DM
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Cross-Industry Standard Process for Data Mining (CRISP-DM)
developed in 1996
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Contributors include DaimlerChrysler, SPSS, and NCR
Developed to fit data mining into general business strategy
Process vendor and tool-neutral
Non-proprietary and freely available
Data mining projects follow iterative, adaptive life cycle consisting of 6
phases
Phase sequences are adaptive
Next, Figure 1.1 illustrates CRISP-DM lifecycle
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Cross Industry Standard Process:
CRISP-DM (cont’d)
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Iterative CRIP-DM process
shown in outer circle
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Most significant dependencies
between phases shown
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Next phase depends on
results from preceding phase
Returning to earlier phase
possible before moving
forward
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Business / Research
Business / Research
Understanding Phase
Understanding Phase
Deployment Phase
Deployment Phase
Evaluation Phase
Evaluation Phase
Data Mining: Concepts and Techniques
Data Understanding
Data Understanding
Phase
Phase
Data Preparation
Data Preparation
Phase
Phase
Modeling Phase
Modeling Phase
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Case Study
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Analyzing Automobile Warranty Claims
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Business Understanding
Objectives include improving customer satisfaction and reducing costs
Manufacturing engineers consulted to formulate business problems
Data mining techniques used to uncover possible issues:
„ Warranty claim interdependencies?
„ Past claims associated with future claims?
„ Association between claim and repair facility?
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Case Study
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(cont’d)
Data Understanding
40GB QUIS database containing 7 million vehicle records used
Vehicle records include manufacturing location, warrant claims, and
additional codes
Database unintelligible to non-domain experts
Costly effort to consult with domain experts from different departments
Data Preparation
QUIS discovered to have limited SQL access
Cases and variables manually extracted
Additional variables derived for modeling phase
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Case Study
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(cont’d)
Proprietary data mining software used
Data format requirements varied for different algorithms
Resulted in exhaustive pre-processing of data
Modeling Phase
Applied Bayesian networks and association rules to uncover dependencies
between warranty claims
Discovered specific combination of construction specifications doubles
probability of electrical cable claim
Investigated whether some garages had more claims than others
Remaining results confidential
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Case Study
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(cont’d)
Evaluation
Researchers disappointed in results
Association rules could not be generalized
Rules “not interesting” according to domain experts
Data models fell short of business objectives
Legacy databases not suited to data mining
Proposal suggested database redesign for future data mining efforts
Deployment
Foregoing effort identified as pilot project, models not deployed
Future data mining efforts planned to integrate more closely to database
systems at DaimlerChrysler
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Case Study
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(cont’d)
Summary
Uncovering hidden nuggets very difficult
During each phase, researchers encountered roadblocks
Applying new data mining effort problematic
Data mining effort requires management support
Substantial human participation required at every stage
Installation, configuration, and data mining modeling not magic
Wrong analysis leads to possibly expensive policy recommendations
No guarantee data mining effort delivers actionable results
However, used properly, data mining may provide profitable results
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Fallacies of Data Mining
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Four Fallacies of Data Mining (Louie, Nautilus Systems, Inc.)
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Fallacy 1
Set of tools can be turned loose on data repositories
Finds answers to all business problems
Reality 1
No automatic data mining tools solve problems
Rather, data mining is process (CRISP-DM)
Integrates into overall business objectives
Fallacy 2
Data mining process is autonomous
Requires little oversight
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Fallacies of Data Mining
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(cont’d)
Reality 2
Requires significant intervention during every phase
After model deployment, new models require updates
Continuous evaluative measures monitored by analysts
Fallacy 3
Data mining quickly pays for itself
Reality 3
Return rates vary
Depending on startup, personnel, data preparation costs, etc.
Fallacy 4
Data mining software easy to use
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Fallacies of Data Mining
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(cont’d)
Reality 4
Ease of use varies across projects
Analysts must combine subject matter knowledge with specific problem
domain
Two Additional Fallacies (Larose)
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Fallacy 5
Data mining identifies causes of business problems
Reality 5
Knowledge discovery process uncovers patterns of behavior
Humans interpret results and identify causes
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Fallacies of Data Mining
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(cont’d)
Fallacy 6
Data mining automatically cleans data in databases
Reality 6
Data mining often uses data from legacy systems
Data possibly not examined or used in years
Organizations starting data mining efforts confronted with huge data
preprocessing task
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Architecture: 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 Kinds of Data?
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Relational database
Data warehouse
Transactional database
Advanced database and information repository
„ Object-relational database
„ Spatial and temporal data
„ Time-series data
„ Stream data
„ Multimedia database
„ Heterogeneous and legacy database
„ Text databases & WWW
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Data Mining Functionalities
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Concept description: Characterization and discrimination
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Association (correlation and causality)
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Generalize, summarize, and contrast data characteristics, e.g., dry
vs. wet regions
Age(20-29) and income(20-30k) -> buys (“cd player”)
[support=2%, confidence=75%]
Classification and Prediction
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Construct models (functions) that describe and distinguish classes
or concepts for future prediction
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E.g., classify countries based on climate, or classify cars based
on gas mileage
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Presentation: decision-tree, classification rule, neural network
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Predict some unknown or missing numerical values
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Data Mining Functionalities (2)
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Cluster analysis
„ Class label is unknown: Group data to form new classes, e.g.,
cluster houses to find distribution patterns
„ Maximizing intra-class similarity & minimizing interclass similarity
Outlier analysis
„ Outlier: a data object that does not comply with the general
behavior of the data
„ Noise or exception? No! useful in fraud detection, rare events
analysis
Trend and evolution analysis
„ Trend and deviation: regression analysis
„ Sequential pattern mining, periodicity analysis
„ Similarity-based analysis
Other pattern-directed or statistical analyses
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What Tasks Can Data Mining
Accomplish?
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Six common data mining tasks
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Description
Estimation
Prediction
Classification
Clustering
Association
(1) Description
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Describes patterns or trends in data
For example, pollster may uncover patterns suggesting those laid-off less
likely to support incumbent
Descriptions of patterns, often suggest possible explanations
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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For example, those laid-off now less financially secure; therefore, prefer
alternate candidate
Data mining models should be transparent
That is, results should be interpretable by humans
Some data mining methods more transparent than others
For example, Decision Trees (transparent) <-> Neural Networks (opaque)
High-quality description accomplished using Exploratory Data Analysis
(EDA)
Graphical method of exploring patterns and trends in data
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What Tasks Can Data Mining
Accomplish? (cont’d)
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(2) Estimation
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Similar to Classification task, except target variable numeric
Models built from complete data records
Records include values for each predictor field and numeric target variable
in training set
For new observations, estimate of target variable made
For example, estimate a patient’s systolic blood pressure, based on
patient’s age, gender, body-mass index, and sodium levels
Here, estimation model built from training set records
Model then estimates value for new case
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Estimation Tasks in Business and Research:
Estimate amount of money, family of four will spend on back-to-school
shopping
Estimate percentage decrease in rotary movement sustained to NFL
player with knee injury
Estimate number of points basketball player scores when double-teamed
in playoffs
Estimate GPA of graduate student, based on student’s undergraduate GPA
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Figure shows scatter plot of graduate GPA against undergraduate GPA
Linear regression finds line (blue) best approximating relationship
between two variables
Regression line estimates student’s graduate GPA based on their
undergraduate GPA
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Minitab statistical software produces regression
equation ŷ = 1.24 + 0.67x
Therefore, estimated student’s graduate GPA = 1.24 plus 0.67 times their
undergraduate GPA
For example, suppose student’s undergraduate GPA = 3.0
According to estimation model
Estimated student’s graduate GPA = 1.24 + 0.67(3.0) = 3.25
Point (x = 3.0, ŷ = 3.25) lies on regression line
Statistical Analysis uses several estimation methods: point estimation,
confidence interval estimation, linear regression and correlation, and
multiple regression
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What Tasks Can Data Mining
Accomplish? (cont’d)
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(3) Prediction
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Similar to classification and estimation, except results lie in the future
Prediction Tasks in Business and Research:
Predict price of stock 3 months into future, based on past performance
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Stock
Price
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Q1
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Q2
Q3
Q4
Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Predict percentage increase in traffic deaths next year, if speed limit
increased
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Predict whether molecule in newly discovered drug leads to profitable
pharmaceutical drug
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Methods used for classification and estimation applicable to prediction
Includes point estimation, confidence interval estimation, linear regression
and correlation, and multiple regression
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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(4) Classification
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Classification requires categorical target variable such as Income Bracket
Three values include “High”, “Middle”, “Low”
Data model examines records containing input fields and target field
Table shows several records from data set
Subject
October 10, 2006
Age
Gender
Occupation
Income Bracket
001
47
F
Software Engineer
High
002
28
M
Marketing Consultant
Middle
003
35
M
Unemployed
Low
…
…
…
…
…
Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Records of persons in data set used to “train” classification model
First, Model built from data records, where value of categorical target
variable (Income Bracket) already known
Algorithm “first learns about” which combinations of input fields are
associated with Income Bracket values in training set
For example, algorithm may determine that older females associated with
high income
Next, trained model examines new records
Information regarding Income Bracket not available
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Based on classifications in training set, new records classified
For example, 63-year old female professor might be classified in “High”
income bracket
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Classification Tasks in Business and Research:
Determine whether credit card transaction fraudulent
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Assessing mortgage application to determine “good” or “bad” credit risk
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Diagnosing whether particular disease present
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Determine if will written by deceased, or fraudulently by someone else
Identify whether certain financial behavior represents terrorist threat
Scatter plot shows Na/K ratio against Age for 200 patients
For example, classify drug type to prescribe based on patient’s age and
sodium/potassium ratio
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Actual drug type prescribed symbolized by shade (light, medium, dark) of
points
Suppose prescription of new patient based on this data set?
Prescribe which drug for young patient with high Na/K ratio?
Young patients plotted on left
High Na/K plotted on upper-half
Quadrant of graph shows light points
Recommended drug = Y (corresponds to light points)
Prescribe which drug for older patient with low Na/K ratio?
Lower-right half of graph shows patients prescribed different drug types
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Data Mining: Concepts and Techniques
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Definitive classification cannot be made
More information required to make decision
Examples show graphs are helpful for understanding two-dimensional
data
However, classification often requires many input attributes
More sophisticated methods of classification required
Commonly used algorithms for classification include k-Nearest Neighbor,
Decision Trees, and Neural Networks
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What Tasks Can Data Mining
Accomplish? (cont’d)
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(5) Clustering
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Refers to grouping records into classes of similar objects
Clustering algorithm seeks to segment data set into homogeneous
subgroups
Where similarity of records in clusters maximized, and similarity to records
outside clusters minimized
Target variable not specified
For example, Claritas, Inc. PRIZM software clusters demographic profiles
for different geographic areas according to zip code
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What Tasks Can Data Mining
Accomplish? (cont’d)
Table shows 62 distinct “lifestyle” types used by PRIZM
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01 Blue Blood Estates
02 Winner's Circle
03 Executive Suites
04 Pools & Patios
05 Kids & Cul-de-Sacs
06 Urban Gold Coast
07 Money & Brains
08 Young Literati
09 American Dreams
10 Bohemian Mix
11 Second City Elite
12 Upward Bound
13 Gray Power
14 Country Squires
15 God's Country
16 Big Fish, Small Pond
17 Greenbelt Families
18 Young Influentials
19 New Empty Nests
20 Boomers & Babies
21 Suburban Sprawl
22 Blue-Chip Blues
23 Upstarts & Seniors
24 New Beginnings
25 Mobility Blues
26 Gray Collars
27 Urban Achievers
28 Big City Blend
29 Old Yankee Rows
30 Mid-City Mix
31 Latino America
32 Middleburg Managers
33 Boomtown Singles
34 Starter Families
35 Sunset City Blues
36 Towns & Gowns
37 New Homesteaders
38 Middle America
39 Red, White & Blues
40 Military Quarters
41 Big Sky Families
42 New Eco - topia
43 River City, USA
44 Shotguns & Pickups
45 Single City Blues
46 Hispanic Mix
47 Inner Cities
48 Smalltown Downtown
49 Hometown Retired
50 Family Scramble
51 Southside City
52 Golden Ponds
53 Rural Industria
54 Norma Rae-Ville
55 Mines & Mills
56 Agri - Business
57 Grain Belt
58 Blue Highways
59 Rustic Elders
60 Back Country Folks
61 Scrub Pine Flats
62 Hard Scrabble
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What Tasks Can Data Mining
Accomplish? (cont’d)
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What do the clusters mean?
According to PRIZM, Clusters for Beverly Hills, CA 90210 include:
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Cluster
Cluster
Cluster
Cluster
01:
10:
02:
08:
Blue Blood Estates
Bohemian Mix
Winner’s Circle
Young Literati
Description of Cluster 01, “...’old money’ heirs that live in America’s
wealthiest suburbs...accustomed to privilege and live luxuriously...”
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What Tasks Can Data Mining
Accomplish? (cont’d)
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Clustering Tasks in Business and Research:
Target marketing niche product for small business that does not have
large marketing budget
For accounting purposes, segment financial behavior into benign and
suspicious categories
Use as dimensionality-reduction tool for data set having several hundred
inputs
Determine gene expression clusters, where many genes exhibit similar
behavior or characteristics
Clustering often used as preliminary step in data mining
Resulting clusters used as input to different technique downstream, such
as neural networks
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What Tasks Can Data Mining
Accomplish? (cont’d)
„
(6) Association
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Find out which attributes “go together”
Market Basket Analysis commonly used in business applications
Quantify relationships in the form of Rules
IF antecedent THEN consequent
Rules measured using support and confidence
For example, discover which items in supermarket are purchased together
Thursday night 200 of 1,000 customers bought diapers, and of those
buying diapers, 50 purchased beer
Association Rule: “IF buy diapers, THEN buy beer”
Support = 200/1,000 = 5%, and confidence = 50/200 = 25%
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What Tasks Can Data Mining
Accomplish? (cont’d)
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„
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Association Tasks in Business and Research:
Investigating proportion of subscribers to cell phone plan responding
positively to service upgrade offer
Predicting degradation in telecommunication networks
Discovering which items in supermarket purchased together
Determining proportion of cases where administering new drug exhibits
serious side effects
Two commonly-used algorithms for generating association rules
A Priori and Generalized Rule Induction (GRI)
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Are All the “Discovered” Patterns Interesting?
„
Data mining 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
„
Objective: based on statistics and structures of patterns, e.g., support,
confidence, etc.
„
Subjective: based on user’s belief in the data, e.g., unexpectedness,
novelty, actionability, etc.
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Can We Find All and Only Interesting Patterns?
„
„
Find all the interesting patterns: Completeness
„
Can a data mining system find all the interesting patterns?
„
Heuristic vs. exhaustive search
„
Association vs. classification vs. clustering
Search for only interesting patterns: An optimization problem
„
Can a data mining system find only the interesting patterns?
„
Approaches
„
First generate all the patterns and then filter out the
uninteresting ones.
„
Generate only the interesting patterns—mining query
optimization
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Data Mining: Confluence of Multiple Disciplines
Database
Systems
Machine
Learning
Algorithm
October 10, 2006
Statistics
Data Mining
Visualization
Other
Disciplines
Data Mining: Concepts and Techniques
57
Data Mining: Classification Schemes
„
„
General functionality
„
Descriptive data mining
„
Predictive data mining
Different views, different classifications
„
Kinds of data to be mined
„
Kinds of knowledge to be discovered
„
Kinds of techniques utilized
„
Kinds of applications adapted
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Multi-Dimensional View of Data Mining
„
Data to be mined
„
„
Knowledge to be mined
„
„
„
Characterization, discrimination, association, classification,
clustering, trend/deviation, outlier analysis, etc.
Multiple/integrated functions and mining at multiple levels
Techniques utilized
„
„
Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media,
heterogeneous, legacy, WWW
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.
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OLAP Mining: Integration of Data Mining and Data Warehousing
„
Data mining systems, DBMS, Data warehouse
systems coupling
„
„
On-line analytical mining data
„
„
No coupling, loose-coupling, semi-tight-coupling, tight-coupling
integration of mining and OLAP technologies
Interactive mining multi-level knowledge
„
Necessity of mining knowledge and patterns at different levels of
abstraction by drilling/rolling, pivoting, slicing/dicing, etc.
„
Integration of multiple mining functions
„
October 10, 2006
Characterized classification, first clustering and then association
Data Mining: Concepts and Techniques
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An OLAM Architecture
Mining query
Mining result
Layer4
User Interface
User GUI API
OLAM
Engine
OLAP
Engine
Layer3
OLAP/OLAM
Data Cube API
Layer2
MDDB
MDDB
Meta Data
Filtering&Integration
Database API
Filtering
Layer1
Data cleaning
Databases
October 10, 2006
Data
Data integration Warehouse
Data Mining: Concepts and Techniques
Data
Repository
61
Major Issues in Data Mining
„
Mining methodology
„
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Performance: efficiency, effectiveness, and scalability
„
Pattern evaluation: the interestingness problem
„
Incorporation of background knowledge
„
Handling noise and incomplete data
„
„
„
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Mining different kinds of knowledge from diverse data types, e.g., bio, stream,
Web
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
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Summary
„
„
„
„
„
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.
„
Data mining systems and architectures
„
Major issues in data mining
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A Brief History of Data Mining Society
„
1989 IJCAI Workshop on Knowledge Discovery in Databases (PiatetskyShapiro)
„
„
Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
1991-1994 Workshops on Knowledge Discovery in Databases
„
Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth,
and R. Uthurusamy, 1996)
„
1995-1998 International Conferences on Knowledge Discovery in Databases
and Data Mining (KDD’95-98)
„
„
Journal of Data Mining and Knowledge Discovery (1997)
1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD
Explorations
„
More conferences on data mining
„
PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.
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Where to Find References?
„
„
„
„
„
Data mining and KDD (SIGKDD: CDROM)
„
Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
„
Journal: Data Mining and Knowledge Discovery, KDD Explorations
Database systems (SIGMOD: CD ROM)
„
Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA
„
Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc.
AI & Machine Learning
„
Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc.
„
Journals: Machine Learning, Artificial Intelligence, etc.
Statistics
„
Conferences: Joint Stat. Meeting, etc.
„
Journals: Annals of statistics, etc.
Visualization
„
Conference proceedings: CHI, ACM-SIGGraph, etc.
„
Journals: IEEE Trans. visualization and computer graphics, etc.
October 10, 2006
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Recommended Reference Books
„
R. Agrawal, J. Han, and H. Mannila, Readings in Data Mining: A Database Perspective, Morgan
Kaufmann (in preparation)
„
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery
and Data Mining. AAAI/MIT Press, 1996
„
U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge
Discovery, Morgan Kaufmann, 2001
„
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001
„
D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001
„
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining,
Inference, and Prediction, Springer-Verlag, 2001
„
T. M. Mitchell, Machine Learning, McGraw Hill, 1997
„
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991
„
S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998
„
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java
Implementations, Morgan Kaufmann, 2001
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