Data Mining:

Concepts and Techniques

(3rd ed.)

— Chapter 1 —

Jiawei Han, Micheline Kamber, and Jian Pei

University of Illinois at Urbana-Champaign &
Simon Fraser University
©2011 Han, Kamber & Pei. All rights reserved.
1
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

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

3
 Evolution of Sciences
 Before 1600, empirical science
 1600-1950s, theoretical science
 Each discipline has grown a theoretical component. Theoretical models often
motivate experiments and generalize our understanding.
 1950s-1990s, computational science
 Over the last 50 years, most disciplines have grown a third, computational branch
(e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)
 Computational Science traditionally meant simulation. It grew out of our inability to
find closed-form solutions for complex mathematical models.
 1990-now, data science
 The flood of data from new scientific instruments and simulations
 The ability to economically store and manage petabytes of data online
 The Internet and computing Grid that makes all these archives universally accessible
 Scientific info. management, acquisition, organization, query, and visualization tasks
scale almost linearly with data volumes. Data mining is a major new challenge!
 Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science,
Comm. ACM, 45(11): 50-54, Nov. 2002

4
 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

5
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
6
 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

7
 Knowledge Discovery (KDD) Process
 This is a view from typical
database systems and data
Pattern Evaluation
warehousing communities
 Data mining plays an essential
role in the knowledge discovery
process Data Mining

Task-relevant Data

Data Warehouse Selection

Data Cleaning

Data Integration

Databases
8
 Example: A Web Mining Framework

 Web mining usually involves

 Data cleaning
 Data integration from multiple sources
 Warehousing the data
 Data cube construction
 Data selection for data mining
 Data mining
 Presentation of the mining results
 Patterns and knowledge to be used or stored into
knowledge-base

9
 A Web Mining Framework: Data Cube

 A data cube is a multi-dimensional data structure.

 A data cube is characterized by its dimensions

(e.g., Products, States, Date).

 Each dimension is associated with corresponding

attributes (for example, the attributes of the
Products dimensions are T-Shirt, Shirt, Jeans and
Jackets)

April 14, 2024 Data Mining: Concepts and Techniques 10

 Data Mining in 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
11
 KDD Process: A Typical View from ML and
Statistics

Input Data Data Pre- Data Post-

Processing Mining Processing

Data integration Pattern discovery Pattern evaluation

Normalization Association & correlation Pattern selection
Feature selection Classification Pattern interpretation
Clustering
Dimension reduction Pattern visualization
Outlier analysis
…………

 This is a view from typical machine learning and statistics communities

12
 Example: Medical Data Mining

 Health care & medical data mining – often

adopted such a view in statistics and machine
learning
 Preprocessing of the data (including feature
extraction and dimension reduction)
 Classification or/and clustering processes
 Post-processing for presentation

13
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
14
 Multi-Dimensional View of Data Mining
 Data to be mined
 Database data (extended-relational, object-oriented, heterogeneous,

legacy), data warehouse, transactional data, stream, spatiotemporal,

time-series, sequence, text and web, multi-media, graphs & social
and information networks
 Knowledge to be mined (or: Data mining functions)
 Characterization, discrimination, association, classification,

clustering, trend/deviation, outlier analysis, etc.

 Descriptive vs. predictive data mining

 Multiple/integrated functions and mining at multiple levels

 Techniques utilized
 Data-intensive, data warehouse (OLAP), machine learning, statistics,

pattern recognition, visualization, high-performance, etc.

 Applications adapted
 Retail, telecommunication, banking, fraud analysis, bio-data mining,

stock market analysis, text mining, Web mining, etc.

15
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
16
 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

17
 Relational Database: Example
 The fictitious AllElectronics is described by the following relation tables:
 customer, item, employee, and branch

 The relation customer consists of a set of attributes describing the customer

information, including a unique customer identity number (cust ID), customer
name, address, age, occupation, annual income, credit information, and
category.

 Similarly, each of the relations item, employee, and branch consists of a set of
attributes describing the properties of these entities.

 Tables can also be used to represent the relationships between or among

multiple entities:
 purchases (customer purchases items, creating a sales transaction
handled by an employee)
 items sold (lists items sold in a given transaction)
 works at (employee works at a branch of AllElectronics)

April 14, 2024 Data Mining: Concepts and Techniques 18

 AllElectronics database
 Relational schema for the AllElectronics relational database, :

April 14, 2024 Data Mining: Concepts and Techniques 19

 Query vs. mining
 Relational data can be accessed by database queries written in a relational
query language (e.g., SQL) .

 A given query is transformed into a set of relational operations, such as join,

selection, and projection. A query allows retrieval of specified subsets of the
data.
 Example: “Show me a list of all items that were sold in the last quarter”

 Relational languages also use aggregate functions such as sum, avg, count,
max, and min. Using aggregates allows you to ask:

 “Show me the total sales of the last month, grouped by branch,”

 “How many sales transactions occurred in the month of December?”
 “Which salesperson had the highest sales?”
 …

April 14, 2024 Data Mining: Concepts and Techniques 20

 Query vs. mining (cont.)
 When mining relational databases, we can go further by searching for trends
or data patterns.

 For example, data mining systems can analyze customer data to predict the
credit risk of new customers based on their income, age, and previous credit
information.

 Data mining systems may also detect deviations—that is, items with sales that
are far from those expected in comparison with the previous year.

 Such deviations can then be further investigated. For example, data mining
may discover that there has been a change in packaging of an item or a
significant increase in price.

April 14, 2024 Data Mining: Concepts and Techniques 21

 Data Warehouse
 Suppose that AllElectronics is a successful international company with
branches around the world. Each branch has its own set of databases.

 The president of AllElectronics has asked you to provide an analysis of the

company’s sales per item type per branch for the third quarter. This is a
difficult task, particularly since the relevant data are spread out over several
databases physically located at numerous sites.

 A data warehouse is a repository of information collected from multiple

sources, stored under a unified schema, and usually residing at a single site.

 Data warehouses are constructed via a process of data cleaning, data

integration, data transformation, data loading, and periodic data refreshing.

April 14, 2024 Data Mining: Concepts and Techniques 22

 Data Warehouse (cont.)
 To facilitate decision making, the data in a data warehouse are organized
around major subjects (e.g., customer, item, supplier, and activity).

 The data are stored to provide information from a historical perspective, such
as in the past 6 to 12 months, and are typically summarized. For example,
rather than storing the details of each sales transaction, the data warehouse
may store a summary of the transactions per item type for each store.

April 14, 2024 Data Mining: Concepts and Techniques 23

 Extract, Transform, and Load
 ETL in data mining consists of the construction of new data subsets
derived from existing data sources.

 ETL stands for the whole process of taking data from various sources
and combining it, transforming it, and loading big data using database
tools.

 Extract is to get data out of different data sources.

 Transform means to change the data format in order to better
support querying and analysis.
 Load is to get this data into a target storage.

 data warehouse is a system that actually performs some ETL

operations: extract, clean, conform and deliver source data into a
dimensional data store and then support and implement querying and
analysis for the purpose of decision making.

April 14, 2024 Data Mining: Concepts and Techniques 24

 Data Lake vs Data Warehouse vs Data Mart

April 14, 2024 Data Mining: Concepts and Techniques Fig© holistics 25
 Data Lake
 A data lake is the place where you dump all forms
of data generated in various parts of your
business: structured data feeds, chat logs, emails,
images (of invoices, receipts, checks etc.), and
videos.

 The data collection routines does not filter any

information out; data related to canceled,
returned, and invalidated transactions will also be
captured, for instance.

April 14, 2024 Data Mining: Concepts and Techniques Fig© holistics 26
 Data Mart
 A data mart is a data storage system that
contains information specific to an organization's
business unit.

 It contains a small and selected part of the data

that the company stores in a larger storage
system.

April 14, 2024 Data Mining: Concepts and Techniques 27

 Data Lake, Warehouse, Mart

April 14, 2024 Data Mining: Concepts and Techniques 28

 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
29
Data Mining Function: (1) Generalization

 Information integration and data warehouse construction

 Data cleaning, transformation, integration, and
multidimensional data model
 Multidimensional concept description: Characterization
and discrimination
 Generalize, summarize, and contrast data
characteristics
 For example, in the AllElectronics store, classes of items for
sale include computers and printers, and concepts of
customers include bigSpenders and budgetSpenders.
 It can be useful to describe individual classes and concepts
in summarized, concise, and yet precise terms.
30
Data Mining Function: (2) Association and
Correlation Analysis
 Frequent patterns (or frequent itemsets)
 What items are frequently purchased together in your
Walmart?
 Association, correlation vs. causality
 A typical association rule
 Bread  Butter [5%, 75%] (support, confidence)
 Are strongly associated items also strongly correlated?
 How to mine such patterns and rules efficiently in large
datasets?
 How to use such patterns for classification, clustering, and
other applications?
31
Data Mining Function: (3) Classification

 Classification and label prediction

 Construct models (functions) based on some training examples
 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 class labels
 Typical methods
 Decision trees, naïve Bayesian classification, support vector
machines, neural networks, rule-based classification, pattern-
based classification, logistic regression, …
 Typical applications:
 Credit card fraud detection, direct marketing, classifying stars,
diseases, web-pages, …

32
Data Mining Function: (4) Cluster Analysis

 Unsupervised learning (i.e., Class label is unknown)

 Group data to form new categories (i.e., clusters), e.g.,
cluster houses to find distribution patterns
 Principle: Maximizing intra-class similarity & minimizing
interclass similarity
 Many methods and applications

33
Data Mining Function: (5) Outlier Analysis
 Outlier analysis
 Outlier: A data object that does not comply with the general
behavior of the data
 Noise or exception? ― One person’s garbage could be another
person’s treasure
 Methods: by product of clustering or regression analysis, …
 Useful in fraud detection, rare events analysis

34
Time and Ordering: Sequential Pattern,
Trend and Evolution Analysis
 Sequence, trend and evolution analysis
 Trend, time-series, and deviation analysis: e.g.,

regression and value prediction

 Sequential pattern mining

 e.g., first buy digital camera, then buy large SD

memory cards
 Periodicity analysis

 Motifs and biological sequence analysis

 Approximate and consecutive motifs

 Similarity-based analysis

 Mining data streams

 Ordered, time-varying, potentially infinite, data streams

35
 Structure and Network Analysis

 Graph mining
 Finding frequent subgraphs (e.g., chemical compounds), trees

(XML), substructures (web fragments)

 Information network analysis
 Social networks: actors (objects, nodes) and relationships (edges)

 e.g., author networks in CS, terrorist networks

 Multiple heterogeneous networks

 A person could be multiple information networks: friends,

family, classmates, …
 Links carry a lot of semantic information: Link mining

 Web mining
 Web is a big information network: from PageRank to Google

 Analysis of Web information networks

 Web community discovery, opinion mining, usage mining, …

36
 Evaluation of Knowledge
 Are all mined knowledge interesting?
 One can mine tremendous amount of “patterns” and knowledge
 Some may fit only certain dimension space (time, location, …)
 Some may not be representative, may be transient, …
 Evaluation of mined knowledge → directly mine only
interesting knowledge?
 Descriptive vs. predictive
 Coverage
 Typicality vs. novelty
 Accuracy
 Timeliness
 …
37
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
38
Data Mining: Confluence of Multiple Disciplines

Machine Pattern Statistics

Learning Recognition

Applications Visualization
Data Mining

Algorithm Database High-Performance

Technology Computing

39
Why Confluence of Multiple Disciplines?

 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
40
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
41
 Applications of Data Mining
 Web page analysis: from web page classification, clustering to
PageRank & HITS algorithms
 Collaborative analysis & recommender systems
 Basket data analysis to targeted marketing
 Biological and medical data analysis: classification, cluster analysis
(microarray data analysis), biological sequence analysis, biological
network analysis
 Data mining and software engineering (e.g., IEEE Computer, Aug.
2009 issue)
 From major dedicated data mining systems/tools (e.g., SAS, MS SQL-
Server Analysis Manager, Oracle Data Mining Tools) to invisible data
mining

42
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
43
 Major Issues in Data Mining (1)

 Mining Methodology
 Mining various and new kinds of knowledge
 Mining knowledge in multi-dimensional space
 Data mining: An interdisciplinary effort
 Boosting the power of discovery in a networked environment
 Handling noise, uncertainty, and incompleteness of data
 Pattern evaluation and pattern- or constraint-guided mining
 User Interaction
 Interactive mining
 Incorporation of background knowledge
 Presentation and visualization of data mining results

44
 Major Issues in Data Mining (2)

 Efficiency and Scalability

 Efficiency and scalability of data mining algorithms
 Parallel, distributed, stream, and incremental mining methods
 Diversity of data types
 Handling complex types of data
 Mining dynamic, networked, and global data repositories
 Data mining and society
 Social impacts of data mining
 Privacy-preserving data mining
 Invisible data mining

45
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
46
 A Brief History of Data Mining Society

 1989 IJCAI Workshop on Knowledge Discovery in Databases

 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)
 ACM SIGKDD conferences since 1998 and SIGKDD Explorations
 More conferences on data mining
 PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM
(2001), etc.
 ACM Transactions on KDD starting in 2007
47
 Conferences and Journals on Data Mining

 KDD Conferences  Other related conferences

 ACM SIGKDD Int. Conf. on  DB conferences: ACM SIGMOD,
Knowledge Discovery in
VLDB, ICDE, EDBT, ICDT, …
Databases and Data Mining (KDD)
 Web and IR conferences: WWW,
 SIAM Data Mining Conf. (SDM)
SIGIR, WSDM
 (IEEE) Int. Conf. on Data Mining
(ICDM)  ML conferences: ICML, NIPS
 European Conf. on Machine  PR conferences: CVPR,
Learning and Principles and  Journals
practices of Knowledge Discovery
 Data Mining and Knowledge
and Data Mining (ECML-PKDD)
Discovery (DAMI or DMKD)
 Pacific-Asia Conf. on Knowledge
Discovery and Data Mining  IEEE Trans. On Knowledge and
(PAKDD) Data Eng. (TKDE)
 Int. Conf. on Web Search and  KDD Explorations
Data Mining (WSDM)  ACM Trans. on KDD

48
 Chapter 1. Introduction
 Why Data Mining?

 What Is Data Mining?

 A Multi-Dimensional View of Data Mining

 What Kind of Data Can Be Mined?

 What Kinds of Patterns Can Be Mined?

 What Technology Are Used?

 What Kind of Applications Are Targeted?

 Major Issues in Data Mining

 A Brief History of Data Mining and Data Mining Society

 Summary
49
 Summary
 Data mining: Discovering interesting patterns and knowledge from
massive amount 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 data
 Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
 Data mining technologies and applications
 Major issues in data mining

50