What Is DATA MINING(INTRODUCTION)

Data Mining:
Concepts and Techniques
— Slides for Textbook —
— Chapter 1 —
October 8, 2014 Data Mining: Concepts and Techniques 1

Data Mining: Concepts and Techniques

Chapter 1. Introduction
 Motivation: Why data mining?
 What is data mining?
 Data Mining: On what kind of data?
 Data mining functionality
 Are all the patterns interesting?
 Classification of data mining systems
 Major issues in data mining

Necessity Is the Mother of Invention
 Data explosion problem
 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
 We are drowning in data, but starving for knowledge!
 Solution: Data warehousing and data mining
 Data warehousing and on-line analytical processing
 Miing interesting knowledge (rules, regularities, patterns,
constraints) from data in large databases

Evolution of Database Technology
 1960s:
 Data collection, database creation, IMS and network DBMS
 1970s:
 Relational data model, relational DBMS implementation
 1980s:
 RDBMS, advanced data models (extended-relational, OO, deductive,
etc.)
 Application-oriented DBMS (spatial, scientific, engineering, etc.)
 1990s:
 Data mining, data warehousing, multimedia databases, and Web
databases
 2000s
 Stream data management and mining
 Data mining with a variety of applications
 Web technology and global information systems

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
 Data mining: a misnomer?
 Alternative names
 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”?
 (Deductive) query processing.
 Expert systems or small ML/statistical programs

Why Data Mining?—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
 DNA and bio-data analysis

Market Analysis and Management
 Where does the data come from?
 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
 Cross-market analysis
 Associations/co-relations between product sales, & prediction based on such association
 Customer profiling
 What types of customers buy what products (clustering or classification)
 Customer requirement analysis
 identifying the best products for different customers
 predict what factors will attract new customers
 Provision of summary information
 multidimensional summary reports
 statistical summary information (data central tendency and variation)

Corporate Analysis & Risk Management
 Finance planning and asset evaluation
 cash flow analysis and prediction
 contingent claim analysis to evaluate assets
 cross-sectional and time series analysis (financial-ratio, 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

Fraud Detection & Mining Unusual Patterns
 Approaches: Clustering & model construction for frauds, outlier analysis
 Applications: Health care, retail, credit card service, telecomm.
 Auto insurance: ring of collisions
 Money laundering: suspicious monetary transactions
 Medical insurance
 Professional patients, ring of doctors, and ring of references
 Unnecessary or correlated screening tests
 Telecommunications: phone-call fraud
 Phone call model: destination of the call, duration, time of day or
week. Analyze patterns that deviate from an expected norm
 Retail industry
 Analysts estimate that 38% of retail shrink is due to dishonest
employees
 Anti-terrorism

Other Applications
 Sports
 IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for
New York Knicks and Miami Heat
 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.

Data Mining: A KDD Process
 Data mining—core of
knowledge discovery
process
Data
Warehouse
Data Cleaning
Pattern Evaluation
Data Mining
Task-relevant Data
Data Integration
Databases
Selection

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

Data Mining and Business Intelligence
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Making
Decisions
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
OLAP, MDA
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP

Architecture: Typical Data Mining
System
Graphical user interface
Pattern evaluation
Data mining engine
Database or data
warehouse server
Data cleaning & data integration Filtering
Data
Warehouse
Databases
Knowledge-base

Data Mining: On What Kinds of Data?
 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

Data Mining Functionalities
 Concept description: Characterization and discrimination
 Generalize, summarize, and contrast data characteristics, e.g., dry
vs. wet regions
 Association (correlation and causality)
 Diaper  Beer [0.5%, 75%]
 Classification and Prediction
 Construct 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
 Predict some unknown or missing numerical values

Data Mining Functionalities (2)
 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

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.

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 general all the patterns and then filter out the
uninteresting ones.
 Generate only the interesting patterns—mining query
optimization

Data Mining: Confluence of Multiple
Disciplines
Database
Systems Statistics
Machine
Learning Data Mining
Visualization
Other
Disciplines
Algorithm

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

Multi-Dimensional View of Data Mining
 Data to be mined
 Relational, data warehouse, transactional, stream, object-oriented/
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.

OLAP Mining: Integration of Data Mining and Data Warehousing
 Data mining systems, DBMS, Data warehouse
systems coupling
 No coupling, loose-coupling, semi-tight-coupling, tight-coupling
 On-line analytical mining data
 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
 Characterized classification, first clustering and then association

An OLAM Architecture
Mining query Mining result
OLAP
Engine
Meta
Data
Filtering&Integration Filtering
Data
Warehouse
User GUI API
Data Cube API
MDDB
OLAM
Engine
Database API
Data cleaning
Data integration
Layer4
User Interface
Layer3
OLAP/OLAM
Layer2
MDDB
Layer1
Data
Repository
Databases

Major Issues in Data Mining
 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

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

A Brief History of Data Mining Society
 1989 IJCAI Workshop on Knowledge Discovery in Databases (Piatetsky-
Shapiro)
 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.

What Is DATA MINING(INTRODUCTION)

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