Introduction on Data Mining basics level

June 3, 2025 Data Mining: Concepts and Technique 1
Data Mining:
Concepts and Techniques
3rd
Edition
— Chapter 1 —
— Introduction —
Courtesy: Jiawei Han and Micheline Kamber
Department of Computer Science
University of Illinois at Urbana-Champaign
www.cs.uiuc.edu/~hanj
©2012 Jiawei Han and Micheline Kamber. All rights reserved.

Course components
 Quiz - 10
 Assignment -10
 Project – 15
 Lab -15
 Mid term -20
 Final - 30
2

(Chapters 1,2,3,4,6,8,9,10,12,13 of This Book)
 Course Coverage
 Introduction
 Getting to Know Your Data
 Data Preprocessing
 Data Warehouse and OLAP Technology: An Introduction
 Mining Frequent Patterns, Association and Correlations
 Classification and Prediction – Basic Concepts
 Cluster Analysis- Basic concepts and Methods
 Outlier Detection
 Data Mining Trends and Research Frontiers

Textbook
 Data Mining:
Concepts and
Techniques
3rd Edition
 Jiawei Han and
Micheline Kamber

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

Example

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, YouTube
 We are drowning in data, but starving for knowledge!
 “Necessity is the mother of invention”—Data mining—Automated
analysis of massive data sets

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

Data vs. Information vs. Knowledge

Data vs. Information vs. Knowledge
Examples:
 Data: student name
 Information: students’ records
 Knowledge: there are two sisters of the
students but they live in two different
apartments.

Knowledge Discovery (KDD) Process
 Data mining—core of
knowledge discovery
process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation

Data Mining Process

Data Mining and Business Intelligence
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Decision
Making
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems

Data Mining: Confluence of Multiple Disciplines
Data Mining
Database
Technology Statistics
Machine
Learning
Pattern
Recognition
Algorithm
Other
Disciplines
Visualization

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

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.

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

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

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

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

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

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.

Find All and Only Interesting Patterns?
 Find all the interesting patterns: Completeness
 Can a data mining system find all the interesting patterns? Do
we need to find all of 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

Architecture: Typical Data Mining System
data cleaning, integration, and selection
Database or Data
Warehouse Server
Data Mining Engine
Pattern Evaluation
Graphical User Interface
Knowledge
-Base
Database Data
Warehouse
World-Wide
Web
Other Info
Repositories

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

Introduction on Data Mining basics level

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