formats in favor of more holistic assessment methods. This is particularly true in business and law, where practical skills like teamwork, negotiation, and communication are becoming increasingly important.7.1. Business Exams:
Emphasis on Real-World ApplicationFuture
Solution Manual
For
Data Mining: Concepts and
Techniques
3rd Edition
by
Jiawei Han, Micheline
Kamber, Jian Pei
The University of Illinois at Urbana-Champaign
Simon Fraser University
,Contents
formats in favor of more holistic assessment methods. This is particularly true in business and law, where practical skills like teamwork, negotiation, and communication are becoming increasingly
important.7.1. Business Exams: Emphasis on Real-World ApplicationFuture
1 Introduction 3
1.1 Exercises ......................................................................................................................................................... 3
1.2 Supplementary Exercises .............................................................................................................................. 7
2 Getting to Know Your Data 11
2.1 Exercises ....................................................................................................................................................... 11
2.2 Supplementary Exercises ............................................................................................................................ 18
3 Data Preprocessing 19
3.1 Exercises ....................................................................................................................................................... 19
3.2 Supplementary Exercises ............................................................................................................................ 31
4 Data Warehousing and Online Analytical Processing 33
4.1 Exercises ....................................................................................................................................................... 33
4.2 Supplementary Exercises ............................................................................................................................ 47
5 Data Cube Technology 49
5.1 Exercises ....................................................................................................................................................... 49
5.2 Supplementary Exercises ............................................................................................................................ 67
6 Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods 69
6.1 Exercises ....................................................................................................................................................... 69
6.2 Supplementary Exercises ............................................................................................................................ 78
7 Advanced Pattern Mining 79
7.1 Exercises ....................................................................................................................................................... 79
7.2 Supplementary Exercises ............................................................................................................................ 88
8 Classification: Basic Concepts 91
8.1 Exercises ....................................................................................................................................................... 91
8.2 Supplementary Exercises ............................................................................................................................ 99
9 Classification: Advanced Methods 101
9.1 Exercises ..................................................................................................................................................... 101
9.2 Supplementary Exercises .......................................................................................................................... 105
10 Cluster Analysis: Basic Concepts and Methods 107
10.1 Exercises ..................................................................................................................................................... 107
10.2 Supplementary Exercises .......................................................................................................................... 115
v
CONTENTS 1
11 Advanced Cluster Analysis 123
11.1 Exercises ..................................................................................................................................................... 123
12 Outlier Detection 127
12.1 Exercises ..................................................................................................................................................... 127
13 Trends and Research Frontiers in Data Mining 131
13.1 Exercises ..................................................................................................................................................... 131
13.2 Supplementary Exercises .......................................................................................................................... 139
,Chapter 1
Introduction
formats in favor of more holistic assessment methods. This is particularly true in business and law, where practical skills like teamwork, negotiation, and communication are becoming increasingly important.7.1. Business Exams:
Emphasis on Real-World ApplicationFuture
1.1 Exercises
1. What is data mining ? In your answer, address the following:
(a) Is it another hype?
(b) Is it a simple transformation or application of technology developed from databases, statistics,
machine learning, and pattern recognition?
(c) We have presented a view that data mining is the result of the evolution of database technology.
Do you think that data mining is also the result of the evolution of machine learning research?
Can you present such views based on the historical progress of this discipline? Do the same for
the fields of statistics and pattern recognition.
(d) Describe the steps involved in data mining when viewed as a process of knowledge discovery.
Answer:
Data mining refers to the process or method that extracts or “mines” interesting knowledge or
patterns from large amounts of data.
(a) Is it another hype?
Data mining is not another hype. Instead, the need for data mining has arisen due to the wide
availability of huge amounts of data and the imminent need for turning such data into useful
information and knowledge. Thus, data mining can be viewed as the result of the natural evolution
of information technology.
(b) Is it a simple transformation of technology developed from databases, statistics, and machine
learning?
No. Data mining is more than a simple transformation of technology developed from databases,
statistics, and machine learning. Instead, data mining involves an integration, rather than a
simple transformation, of techniques from multiple disciplines such as database technology, statis-
tics, machine learning, high-performance computing, pattern recognition, neural networks, data
visualization, information retrieval, image and signal processing, and spatial data analysis.
(c) Explain how the evolution of database technology led to data mining.
Database technology began with the development of data collection and database creation mech-
anisms that led to the development of effective mechanisms for data management including data
storage and retrieval, and query and transaction processing. The large number of database sys-
tems offering query and transaction processing eventually and naturally led to the need for data
analysis and understanding. Hence, data mining began its development out of this necessity.
3
, 4
Describe the steps involved in data mining when viewed as a process of knowledge discovery. The steps involved in data
mining when viewed as a process of knowledge discovery are as follows:
• Data cleaning, a process that removes or transforms noise and inconsistent data
• Data integration, where multiple data sources may be combined
• Data selection, where data relevant to the analysis task are retrieved from the database
Data transformation, where data are transformed or consolidated into forms appropriate
for mining
Data mining, an essential process where intelligent and efficient methods are applied in
order to extract patterns
Pattern evaluation, a process that identifies the truly interesting patterns representing
knowledge based on some interestingness measures
Knowledge presentation, where visualization and knowledge representation techniques are
used to present the mined knowledge to the user
formats in favor of more holistic assessment methods. This is particularly true in business and law, where practical skills like teamwork, negotiation, and communication are becoming increasingly important.7.1. Business Exams:
Emphasis on Real-World ApplicationFuture
2. How is a data warehouse different from a database? How are they similar?
Answer:
Differences between a data warehouse and a database: A data warehouse is a repository of informa-
tion collected from multiple sources, over a history of time, stored under a unified schema, and used
for data analysis and decision support; whereas a database, is a collection of interrelated data that
represents the current status of the stored data. There could be multiple heterogeneous databases
where the schema of one database may not agree with the schema of another. A database system
supports ad-hoc query and on-line transaction processing. For more details, please refer to the section
“Differences between operational database systems and data warehouses.”
Similarities between a data warehouse and a database: Both are repositories of information, storing
huge amounts of persistent data.
3. Define each of the following data mining functionalities: characterization, discrimination, association
and correlation analysis, classification, regression, clustering, and outlier analysis. Give examples of
each data mining functionality, using a real-life database that you are familiar with.
Answer:
Characterization is a summarization of the general characteristics or features of a target class of
data. For example, the characteristics of students can be produced, generating a profile of all the
University first year computing science students, which may include such information as a high GPA
and large number of courses taken.
Discrimination is a comparison of the general features of target class data objects with the general
features of objects from one or a set of contrasting classes. For example, the general features of students
with high GPA’s may be compared with the general features of students with low GPA’s. The resulting
description could be a general comparative profile of the students such as 75% of the students with
high GPA’s are fourth-year computing science students while 65% of the students with low GPA’s are
not.
Association is the discovery of association rules showing attribute-value conditions that occur fre-
quently together in a given set of data. For example, a data mining system may find association rules
like
major(X, “computing science””) ⇒ owns(X, “personal computer”)
[support = 12%, confidence = 98%]
Emphasis on Real-World ApplicationFuture
Solution Manual
For
Data Mining: Concepts and
Techniques
3rd Edition
by
Jiawei Han, Micheline
Kamber, Jian Pei
The University of Illinois at Urbana-Champaign
Simon Fraser University
,Contents
formats in favor of more holistic assessment methods. This is particularly true in business and law, where practical skills like teamwork, negotiation, and communication are becoming increasingly
important.7.1. Business Exams: Emphasis on Real-World ApplicationFuture
1 Introduction 3
1.1 Exercises ......................................................................................................................................................... 3
1.2 Supplementary Exercises .............................................................................................................................. 7
2 Getting to Know Your Data 11
2.1 Exercises ....................................................................................................................................................... 11
2.2 Supplementary Exercises ............................................................................................................................ 18
3 Data Preprocessing 19
3.1 Exercises ....................................................................................................................................................... 19
3.2 Supplementary Exercises ............................................................................................................................ 31
4 Data Warehousing and Online Analytical Processing 33
4.1 Exercises ....................................................................................................................................................... 33
4.2 Supplementary Exercises ............................................................................................................................ 47
5 Data Cube Technology 49
5.1 Exercises ....................................................................................................................................................... 49
5.2 Supplementary Exercises ............................................................................................................................ 67
6 Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods 69
6.1 Exercises ....................................................................................................................................................... 69
6.2 Supplementary Exercises ............................................................................................................................ 78
7 Advanced Pattern Mining 79
7.1 Exercises ....................................................................................................................................................... 79
7.2 Supplementary Exercises ............................................................................................................................ 88
8 Classification: Basic Concepts 91
8.1 Exercises ....................................................................................................................................................... 91
8.2 Supplementary Exercises ............................................................................................................................ 99
9 Classification: Advanced Methods 101
9.1 Exercises ..................................................................................................................................................... 101
9.2 Supplementary Exercises .......................................................................................................................... 105
10 Cluster Analysis: Basic Concepts and Methods 107
10.1 Exercises ..................................................................................................................................................... 107
10.2 Supplementary Exercises .......................................................................................................................... 115
v
CONTENTS 1
11 Advanced Cluster Analysis 123
11.1 Exercises ..................................................................................................................................................... 123
12 Outlier Detection 127
12.1 Exercises ..................................................................................................................................................... 127
13 Trends and Research Frontiers in Data Mining 131
13.1 Exercises ..................................................................................................................................................... 131
13.2 Supplementary Exercises .......................................................................................................................... 139
,Chapter 1
Introduction
formats in favor of more holistic assessment methods. This is particularly true in business and law, where practical skills like teamwork, negotiation, and communication are becoming increasingly important.7.1. Business Exams:
Emphasis on Real-World ApplicationFuture
1.1 Exercises
1. What is data mining ? In your answer, address the following:
(a) Is it another hype?
(b) Is it a simple transformation or application of technology developed from databases, statistics,
machine learning, and pattern recognition?
(c) We have presented a view that data mining is the result of the evolution of database technology.
Do you think that data mining is also the result of the evolution of machine learning research?
Can you present such views based on the historical progress of this discipline? Do the same for
the fields of statistics and pattern recognition.
(d) Describe the steps involved in data mining when viewed as a process of knowledge discovery.
Answer:
Data mining refers to the process or method that extracts or “mines” interesting knowledge or
patterns from large amounts of data.
(a) Is it another hype?
Data mining is not another hype. Instead, the need for data mining has arisen due to the wide
availability of huge amounts of data and the imminent need for turning such data into useful
information and knowledge. Thus, data mining can be viewed as the result of the natural evolution
of information technology.
(b) Is it a simple transformation of technology developed from databases, statistics, and machine
learning?
No. Data mining is more than a simple transformation of technology developed from databases,
statistics, and machine learning. Instead, data mining involves an integration, rather than a
simple transformation, of techniques from multiple disciplines such as database technology, statis-
tics, machine learning, high-performance computing, pattern recognition, neural networks, data
visualization, information retrieval, image and signal processing, and spatial data analysis.
(c) Explain how the evolution of database technology led to data mining.
Database technology began with the development of data collection and database creation mech-
anisms that led to the development of effective mechanisms for data management including data
storage and retrieval, and query and transaction processing. The large number of database sys-
tems offering query and transaction processing eventually and naturally led to the need for data
analysis and understanding. Hence, data mining began its development out of this necessity.
3
, 4
Describe the steps involved in data mining when viewed as a process of knowledge discovery. The steps involved in data
mining when viewed as a process of knowledge discovery are as follows:
• Data cleaning, a process that removes or transforms noise and inconsistent data
• Data integration, where multiple data sources may be combined
• Data selection, where data relevant to the analysis task are retrieved from the database
Data transformation, where data are transformed or consolidated into forms appropriate
for mining
Data mining, an essential process where intelligent and efficient methods are applied in
order to extract patterns
Pattern evaluation, a process that identifies the truly interesting patterns representing
knowledge based on some interestingness measures
Knowledge presentation, where visualization and knowledge representation techniques are
used to present the mined knowledge to the user
formats in favor of more holistic assessment methods. This is particularly true in business and law, where practical skills like teamwork, negotiation, and communication are becoming increasingly important.7.1. Business Exams:
Emphasis on Real-World ApplicationFuture
2. How is a data warehouse different from a database? How are they similar?
Answer:
Differences between a data warehouse and a database: A data warehouse is a repository of informa-
tion collected from multiple sources, over a history of time, stored under a unified schema, and used
for data analysis and decision support; whereas a database, is a collection of interrelated data that
represents the current status of the stored data. There could be multiple heterogeneous databases
where the schema of one database may not agree with the schema of another. A database system
supports ad-hoc query and on-line transaction processing. For more details, please refer to the section
“Differences between operational database systems and data warehouses.”
Similarities between a data warehouse and a database: Both are repositories of information, storing
huge amounts of persistent data.
3. Define each of the following data mining functionalities: characterization, discrimination, association
and correlation analysis, classification, regression, clustering, and outlier analysis. Give examples of
each data mining functionality, using a real-life database that you are familiar with.
Answer:
Characterization is a summarization of the general characteristics or features of a target class of
data. For example, the characteristics of students can be produced, generating a profile of all the
University first year computing science students, which may include such information as a high GPA
and large number of courses taken.
Discrimination is a comparison of the general features of target class data objects with the general
features of objects from one or a set of contrasting classes. For example, the general features of students
with high GPA’s may be compared with the general features of students with low GPA’s. The resulting
description could be a general comparative profile of the students such as 75% of the students with
high GPA’s are fourth-year computing science students while 65% of the students with low GPA’s are
not.
Association is the discovery of association rules showing attribute-value conditions that occur fre-
quently together in a given set of data. For example, a data mining system may find association rules
like
major(X, “computing science””) ⇒ owns(X, “personal computer”)
[support = 12%, confidence = 98%]
