Data Mining:

Concepts and
Techniques
— Chapter 1 —
— Introduction —

Jiawei Han and Micheline Kamber

Department of Computer Science
University of Illinois at Urbana-Champaign
1
 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

◼ Data Mining Task Primitives

◼ Integration of data mining system with a DB and DW System

◼ Major issues in data mining

August 22, 2024 Data Mining: Concepts and Techniques 2

 Why Data Mining?

◼ The Explosive Growth of Data: from terabytes to petabytes

◼ Data collection and data availability
◼ Automated data collection tools, database systems, Web,
computerized society
◼ Major sources of abundant data
◼ Business: Web, e-commerce, transactions, stocks, …
◼ Science: Remote sensing, bioinformatics, scientific simulation, …
◼ Society and everyone: news, digital cameras,
◼ We are drowning in data, but starving for knowledge!
◼ “Necessity is the mother of invention”—Data mining—Automated
analysis of massive data sets

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 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 and its applications
◼ Web technology (XML, data integration) and global information systems

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 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”?
◼ Simple search and query processing
◼ (Deductive) expert systems

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 Knowledge Discovery (KDD) Process

◼ Data mining—core of Pattern Evaluation

knowledge discovery
process
Data Mining

Task-relevant Data

Data Warehouse Selection

Data Cleaning

Data Integration

Databases
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 KDD Process
Step 1: Goal Identification
Defined
Goals

Step 2: Create Target Data May

take
Step 3: Data Preprocessing
Data
Warehouse Cleansed

60% of
Data

Target

effort
Transactional Data
Database
Step 4: Data Transformation

Transformed
Flat
Data
File

Step 6: Interpretation & Evaluation

Step 5: Data Mining

Data
Model

Step 7: Taking Action

 KDD Process: Several Key Steps
◼ 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
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 Data Mining and Business Intelligence

Increasing potential
to support
business decisions End User
Decision
Making

Data Presentation Business

Analyst
Visualization Techniques
Data Mining Data
Information Discovery Analyst

Data Exploration
Statistical Summary, Querying, and Reporting

Data Preprocessing/Integration, Data Warehouses

DBA
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
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 Data Mining: Confluence of Multiple Disciplines

Database
Technology Statistics

Machine Visualization
Learning Data Mining

Pattern
Recognition Other
Algorithm Disciplines

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 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
◼ Bioinformatics and bio-data analysis

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 Why Not Traditional Data Analysis?
◼ Tremendous amount of data
◼ Algorithms must be highly scalable to handle such as tera-bytes of
data
◼ High-dimensionality of data
◼ Micro-array may have tens of thousands of dimensions
◼ High complexity of data
◼ Data streams and sensor data
◼ Time-series data, temporal data, sequence data
◼ Structure data, graphs, social networks and multi-linked data
◼ Heterogeneous databases and legacy databases
◼ Spatial, spatiotemporal, multimedia, text and Web data
◼ Software programs, scientific simulations
◼ New and sophisticated applications
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 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, text mining, Web mining, etc.

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 Data Mining: Classification Schemes

◼ General functionality
◼ Descriptive data mining
◼ Predictive data mining
◼ Different views lead to different classifications
◼ Data view: Kinds of data to be mined
◼ Knowledge view: Kinds of knowledge to be discovered
◼ Method view: Kinds of techniques utilized
◼ Application view: Kinds of applications adapted

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 Data Mining: On What Kinds of Data?
◼ Database-oriented data sets and applications
◼ Relational database, data warehouse, transactional database
◼ Advanced data sets and advanced applications
◼ Data streams and sensor data
◼ Time-series data, temporal data, sequence data (incl. bio-sequences)
◼ Structure data, graphs, social networks and multi-linked data
◼ Object-relational databases
◼ Heterogeneous databases and legacy databases
◼ Spatial data and spatiotemporal data
◼ Multimedia database
◼ Text databases
◼ The World-Wide Web

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 Data Mining Functionalities
◼ Multidimensional concept description: Characterization and
discrimination
◼ Generalize, summarize, and contrast data characteristics, e.g., dry
vs. wet regions
◼ Frequent patterns, association, correlation vs. causality
◼ Diaper → Beer [0.5%, 75%] (Correlation or causality?)
◼ 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)
◼ Predict some unknown or missing numerical values

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 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: Data object that does not comply with the general behavior

of the data
◼ Noise or exception? Useful in fraud detection, rare events analysis

◼ Trend and evolution analysis

◼ Trend and deviation: e.g., regression analysis

◼ Sequential pattern mining: e.g., digital camera → large SD memory

◼ Periodicity analysis

◼ Similarity-based analysis

◼ Other pattern-directed or statistical analyses

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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

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Coupling Data Mining with DB/DW Systems

◼ No coupling—flat file processing, not recommended

◼ Loose coupling
◼ Fetching data from DB/DW
◼ Semi-tight coupling—enhanced DM performance
◼ Provide efficient implement a few data mining primitives in a
DB/DW system, e.g., sorting, indexing, aggregation, histogram
analysis, multiway join, precomputation of some stat functions
◼ Tight coupling—A uniform information processing
environment
◼ DM is smoothly integrated into a DB/DW system, mining query
is optimized based on mining query, indexing, query processing
methods, etc.
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 Architecture: Typical Data Mining System

Graphical User Interface

Pattern Evaluation
Knowl
Data Mining Engine edge-
Base
Database or Data
Warehouse Server

data cleaning, integration, and selection

Data World-Wide Other Info

Database Repositories
Warehouse Web

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 Ex. 1: 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—Find associations/co-relations between product sales,
& predict based on such association
◼ Customer profiling—What types of customers buy what products (clustering
or classification)
◼ Customer requirement analysis
◼ Identify 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)

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 Ex. 2: 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

August 22, 2024 Data Mining: Concepts and Techniques 22

Ex. 3: 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

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