Download Data Mining

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
©2013 Han, Kamber & Pei. All rights reserved.
1
Chapter 1. Introduction

What Is Data Mining ?

Why Data Mining?

A Multi-Dimensional View of Data Mining

What Kinds of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Kinds of Technologies Are Used?

What Kinds of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
2
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
Alternative names

Knowledge discovery in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology,
data dredging, information harvesting, business
intelligence, etc.
3
What Is Data Mining?


If you are doing Gold Mining it means you are
looking for Gold in Rocks. Data Mining suggest
that we are looking for data but in fact we are
looking for KNOWLEDGE in the rocks of DATA
therefore ....
Data mining should have been appropriately
named “knowledge mining from data” or
“knowledge mining”.
4
Chapter 1. Introduction

What Is Data Mining ?

Why Data Mining?

A Multi-Dimensional View of Data Mining

What Kinds of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Kinds of Technologies Are Used?

What Kinds of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
5
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, sales promotions,
company profiles and performance, and customer feedback …
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
6
Figure 1.2
The world is data rich but information poor.
7
Figure 1.3
Data mining—searching for knowledge (interesting patterns) in data
8
Why Data Mining?
Credit ratings/targeted marketing:
Given a database of 100,000 names, which persons are the least
likely to default on their credit cards?
Identify likely responders to sales promotions
 Fraud detection
Which types of transactions are likely to be fraudulent, given the
demographics and transactional history of a particular customer?
 Customer relationship management:
Which of my customers are likely to be the most loyal, and which
are most likely to leave for a competitor? :

Data Mining helps extract such information
9
Knowledge Discovery in Data
(KDD)

Knowledge Discovery in Data (KDD) is a process of
which data mining forms just one part, albite a central
one.
10
Knowledge Discovery in Data
(KDD) Process
• Data mining plays an essential
role in the knowledge discovery
Pattern Evaluation
process
• Data mining as a step in the
process of knowledge
Data Mining
discovery.
Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases
11
The knowledge discovery(KDD) process

The knowledge discovery process usually involves iterative
sequence of the following steps:
1.
Data integration- to combine multiple source
2.
Data cleaning- to remove noise and inconsistent data
3.
Data selection - to retrieve relevant data for analysis
4.
Data transformation - to transform data into appropriate
form for data mining
5.
Data mining- Implementing techniques to extract patterns
of interest
6.
Pattern evaluation - Where the mined data are being tested
and assessed for understanding the synthesized knowledge
and its range of validity
7.
knowledge presentation- Presenting the results to the
decision-maker
12
Data Mining in Business Intelligence
Increasing potential
to support
business decisions
Decision
Making
Data Presentation
Visualization Techniques
End User
Business
Analyst
Data Mining
Information Discovery
Data
Analyst
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
DBA
13
Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kinds of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Kinds of Technologies Are Used?

What Kinds of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
14
Data Can Be Mined




Data to be mined
 Database data (extended-relational, object-oriented, heterogeneous,
legacy), data warehouse, transactional data, stream, 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?

What Kinds of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Kinds of Technologies Are Used?

What Kinds of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
16
Data Mining Functionalities
Data Mining functionalities are used to specify the
kind of patterns to be found in data mining tasks.


Data Mining tasks can be classified into two
categories:
1- Descriptive tasks .
2- Predictive tasks.
 Descriptive Tasks can help an organization to understand its customers,
but predictive Tasks is necessary to facilitate the desired outcomes. Both
descriptive and predictive modeling constitute key elements of data
mining and Web mining.
17
1- Descriptive

Characterize general properties of data in the database.
Objective: To derive patterns (correlation ,trends ) that
summarizes the underlying relationship between data.
Examples:




Identifying web pages that are accessed together.(human interpretable
pattern).
Categorize customers by their product preferences and life stage.(identify
many different relationships between customers or products).
customer groups are clustered according to demographics, purchasing
behavior, expressed interests and other descriptive factors.
18
2- Predictive

perform inference on data to make predictions.
Objective: Predict the value of a specific attribute
(target/dependent variable)based on the value of other
attributes (explanatory).
 The core of predictive task relies on capturing
relationships between explanatory variables and the
predicted variables from past occurrences, and
exploiting it to predict future outcomes
Example: Judge if a patient has specific
disease based on his/her medical tests results.

19
20
Data Mining Function: (1) Association and
Correlation Analysis

Frequent patterns (or frequent item sets)

A frequent item set typically refers to a set of items that often appear
together in a transactional data set.


What items are frequently purchased together in your Walmart?

example, milk and bread, which are frequently bought together in
grocery stores by many customers.

Mining frequent patterns leads to the discovery of interesting
associations and correlations within data.
Association, correlation vs. causality

A typical association rule


Computer  Software [0.5%, 75%] (support, confidence)
Are strongly associated items also strongly correlated?
21
Data Mining Function: (2) Classification

Classification and label prediction (Predictive)
Is the process of construct models (or function) that describes and distinguishes
data classes or concepts for future prediction. The model is derived based on
the analysis of a set of training data and is used to predict the class label of
objects (data) for which the class label is unknown.


Typical methods


E.g., classify countries based on (climate), or classify cars
based on (gas mileage)
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, …
22
Data Mining Function: (2) Classification
“How is the derived model presented?” The derived model may be represented in
various forms, such as classification rules (i.e., IF-THEN rules), decision trees,
mathematical formulae, or neural networks
IF-THEN Rules
Decision Tree
For example, a company wants to classify their prospect customers. When a new customer
comes, they have to determine if this is a customer who is going to buy their products or
not.
23
Data Mining Function: (3) 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

Clustering can be used to generate class labels for a group of
data which did not exist at the beginning.

Principle: Maximizing intra-class similarity & minimizing interclass similarity
 Data points in one cluster are more similar to one another.
 Data points in separate clusters are less similar to one
another.
24
Data Mining Function: (4) 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

Useful in fraud detection, rare events analysis
25
Are All Patterns Interesting?


A data mining system has the potential to generate
thousands or even millions of patterns, or rules
We need to answer three questions to say if patterns
are interesting
1. What makes a pattern interesting?
2. Can a data mining system generate all of the
interesting patterns?
3. Can the system generate only the interesting
ones?
26
Are All Patterns Interesting?
What makes a pattern is interesting?
a pattern is interesting if it is easily understood by humans,
Novel, Potentially useful or desired, and valid
Not known before
validates a hypothesis that user
sought to confirm
Valid on new set of data with a degree of
certainty
27
Are All Patterns Interesting?
Can a data mining system generate all of the
interesting patterns?
A data mining algorithm is complete if it mines all
interesting patterns.

It is often unrealistic and inefficient for data mining
systems to generate all possible patterns. Instead, userprovided constraints and interestingness measures
should be used to focus the search.

For some mining tasks, such as association, this is
often sufficient to ensure the completeness of the
algorithm.

28
Can a data mining system generate only interesting
patterns?
A data mining algorithm is consistent if it mines only interesting
patterns. It is an optimization problem.
It is highly desirable for data mining systems to generate only
interesting patterns. This would be efficient for users and data
mining systems because neither would have to search through
the patterns generated to identify the truly interesting ones.
Sufficient progress has been made in this direction, but it still a
challenging issue in data mining.
29
Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kinds of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Kinds of Technologies Are Used?

What Kinds of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
30
Data Mining: Confluence of Multiple Disciplines
Machine
Learning
Applications
Algorithm
Pattern
Recognition
Data Mining
Database
Technology
Statistics
Visualization
High-Performance
Computing
31
Why Confluence of Multiple Disciplines?




Tremendous amount of data
 Algorithms must be scalable to handle big 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 and information networks
 Spatial, spatiotemporal, multimedia, text and Web data
 Software programs, scientific simulations
New and sophisticated applications
32
Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kinds of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Kinds of Technologies Are Used?

What Kinds of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
33
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

From major dedicated data mining systems/tools (e.g., SAS, MS SQLServer Analysis Manager, Oracle Data Mining Tools) to invisible data
mining
34
Summary

Data mining: Discovering interesting patterns and knowledge from massive
amount of data

A natural evolution of science and information 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, trend and outlier analysis, etc.

Data mining technologies and applications

Major issues in data mining
35
April 29, 2017
Data Mining: Concepts and Techniques
36
A Brief History of Data Mining Society

1989 IJCAI Workshop on Knowledge Discovery in Databases


1991-1994 Workshops on Knowledge Discovery in Databases


Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. PiatetskyShapiro, 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),
WSDM (2008), etc.
ACM Transactions on KDD (2007)
37
Conferences and Journals on Data Mining

KDD Conferences

ACM SIGKDD Int. Conf. on
Knowledge Discovery in Databases
and Data Mining (KDD)

SIAM Data Mining Conf. (SDM)

(IEEE) Int. Conf. on Data Mining
(ICDM)

European Conf. on Machine Learning
and Principles and practices of
Knowledge Discovery and Data
Mining (ECML-PKDD)

Pacific-Asia Conf. on Knowledge
Discovery and Data Mining (PAKDD)

Int. Conf. on Web Search and Data
Mining (WSDM)

Other related conferences



DB conferences: ACM SIGMOD,
VLDB, ICDE, EDBT, ICDT, …
Web and IR conferences: WWW,
SIGIR, WSDM

ML conferences: ICML, NIPS

PR conferences: CVPR,
Journals


Data Mining and Knowledge
Discovery (DAMI or DMKD)
IEEE Trans. On Knowledge and
Data Eng. (TKDE)

KDD Explorations

ACM Trans. on KDD
38
Recommended Reference Books

E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011

S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000

T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003

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, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed. , 2011

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.,
Springer, 2009

B. Liu, Web Data Mining, Springer 2006

T. M. Mitchell, Machine Learning, McGraw Hill, 1997

Y. Sun and J. Han, Mining Heterogeneous Information Networks, Morgan & Claypool, 2012

P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005

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, 2nd ed. 2005
39