All Chapters Covered
SOLUTION MANUAL
, @SOLUTIONSSTUDY
Contents
1 Introduction 3
1.11 Exercises ......................................................................................................................................................... 3
2 Data Preprocessing 13
2.8 Exercises ....................................................................................................................................................... 13
3 Data Warehouse and OLAP Technology: An Overview 31
3.7 Exercises ....................................................................................................................................................... 31
4 Data Cube Computation and Data Generalization 41
4.5 Exercises ....................................................................................................................................................... 41
5 Mining Frequent Patterns, Associations, and Correlations 53
5.7 Exercises ....................................................................................................................................................... 53
6 Classification and Prediction 69
6.17 Exercises ....................................................................................................................................................... 69
7 Cluster Analysis 79
7.13 Exercises ....................................................................................................................................................... 79
8 Mining Stream, Time-Series, and Sequence Data 91
8.6 Exercises ....................................................................................................................................................... 91
9 Graph Mining, Social Network Analysis, and Multirelational Data Mining 103
9.5 Exercises ..................................................................................................................................................... 103
10 Mining Object, Spatial, Multimedia, Text, and Web Data 111
10.7 Exercises ..................................................................................................................................................... 111
11 Applications and Trends in Data Mining 123
11.7 Exercises ..................................................................................................................................................... 123
1
,Chapter 1
Introduction
1.11 Exercises
1.1. What is data mining? In your answer, address the following:
(a) Is it another hype?
(b) Is it a simple transformation of technology developed from databases, statistics, and machine learning?
(c) Explain how the evolution of database technology led to data mining.
(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, sta -
tistics, and machine learning. Instead, data mining involves an integration, rather than a simple
transformation, of techniques from multiple disciplines such as database technology, statistics, ma-
chine 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 mechanisms
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 systems 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.
(d) 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
3
, 4 CHAPTER 1. INTRODUCTION
• 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 knowl-
edge based on some interestingness measures
• Knowledge presentation, where visualization and knowledge representation techniques are used to
present the mined knowledge to the user
1.2. Present an example where data mining is crucial to the success of a business. What data mining functions
does this business need? Can they be performed alternatively by data query processing or simple statistical
analysis?
Answer:
A department store, for example, can use data mining to assist with its target marketing mail campaign. Using
data mining functions such as association, the store can use the mined strong association rules to determine
which products bought by one group of customers are likely to lead to the buying of certain other products.
With this information, the store can then mail marketing materials only to those kinds of customers who
exhibit a high likelihood of purchasing additional products. Data query processing is used for data or
information retrieval and does not have the means for finding association rules. Similarly, simple statistical
analysis cannot handle large amounts of data such as those of customer records in a department store.
1.3. Suppose your task as a software engineer at Big-University is to design a data mining system to examine their
university course database, which contains the following information: the name, address, and status (e.g.,
undergraduate or graduate) of each student, the courses taken, and their cumulative grade point average
(GPA). Describe the architecture you would choose. What is the purpose of each component of this
architecture?
Answer:
A data mining architecture that can be used for this application would consist of the following major
components:
• A database, data warehouse, or other information repository, which consists of the set of databases,
data warehouses, spreadsheets, or other kinds of information repositories containing the student and
course information.
• A database or data warehouse server, which fetches the relevant data based on the users’ data
mining requests.
• A knowledge base that contains the domain knowledge used to guide the search or to evaluate the
interestingness of resulting patterns. For example, the knowledge base may contain concept hierarchies
and metadata (e.g., describing data from multiple heterogeneous sources).
• A data mining engine, which consists of a set of functional modules for tasks such as classification,
association, classification, cluster analysis, and evolution and deviation analysis.
• A pattern evaluation module that works in tandem with the data mining modules by employing
interestingness measures to help focus the search towards interesting patterns.
• A graphical user interface that provides the user with an interactive approach to the data mining
system.
SOLUTION MANUAL
, @SOLUTIONSSTUDY
Contents
1 Introduction 3
1.11 Exercises ......................................................................................................................................................... 3
2 Data Preprocessing 13
2.8 Exercises ....................................................................................................................................................... 13
3 Data Warehouse and OLAP Technology: An Overview 31
3.7 Exercises ....................................................................................................................................................... 31
4 Data Cube Computation and Data Generalization 41
4.5 Exercises ....................................................................................................................................................... 41
5 Mining Frequent Patterns, Associations, and Correlations 53
5.7 Exercises ....................................................................................................................................................... 53
6 Classification and Prediction 69
6.17 Exercises ....................................................................................................................................................... 69
7 Cluster Analysis 79
7.13 Exercises ....................................................................................................................................................... 79
8 Mining Stream, Time-Series, and Sequence Data 91
8.6 Exercises ....................................................................................................................................................... 91
9 Graph Mining, Social Network Analysis, and Multirelational Data Mining 103
9.5 Exercises ..................................................................................................................................................... 103
10 Mining Object, Spatial, Multimedia, Text, and Web Data 111
10.7 Exercises ..................................................................................................................................................... 111
11 Applications and Trends in Data Mining 123
11.7 Exercises ..................................................................................................................................................... 123
1
,Chapter 1
Introduction
1.11 Exercises
1.1. What is data mining? In your answer, address the following:
(a) Is it another hype?
(b) Is it a simple transformation of technology developed from databases, statistics, and machine learning?
(c) Explain how the evolution of database technology led to data mining.
(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, sta -
tistics, and machine learning. Instead, data mining involves an integration, rather than a simple
transformation, of techniques from multiple disciplines such as database technology, statistics, ma-
chine 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 mechanisms
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 systems 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.
(d) 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
3
, 4 CHAPTER 1. INTRODUCTION
• 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 knowl-
edge based on some interestingness measures
• Knowledge presentation, where visualization and knowledge representation techniques are used to
present the mined knowledge to the user
1.2. Present an example where data mining is crucial to the success of a business. What data mining functions
does this business need? Can they be performed alternatively by data query processing or simple statistical
analysis?
Answer:
A department store, for example, can use data mining to assist with its target marketing mail campaign. Using
data mining functions such as association, the store can use the mined strong association rules to determine
which products bought by one group of customers are likely to lead to the buying of certain other products.
With this information, the store can then mail marketing materials only to those kinds of customers who
exhibit a high likelihood of purchasing additional products. Data query processing is used for data or
information retrieval and does not have the means for finding association rules. Similarly, simple statistical
analysis cannot handle large amounts of data such as those of customer records in a department store.
1.3. Suppose your task as a software engineer at Big-University is to design a data mining system to examine their
university course database, which contains the following information: the name, address, and status (e.g.,
undergraduate or graduate) of each student, the courses taken, and their cumulative grade point average
(GPA). Describe the architecture you would choose. What is the purpose of each component of this
architecture?
Answer:
A data mining architecture that can be used for this application would consist of the following major
components:
• A database, data warehouse, or other information repository, which consists of the set of databases,
data warehouses, spreadsheets, or other kinds of information repositories containing the student and
course information.
• A database or data warehouse server, which fetches the relevant data based on the users’ data
mining requests.
• A knowledge base that contains the domain knowledge used to guide the search or to evaluate the
interestingness of resulting patterns. For example, the knowledge base may contain concept hierarchies
and metadata (e.g., describing data from multiple heterogeneous sources).
• A data mining engine, which consists of a set of functional modules for tasks such as classification,
association, classification, cluster analysis, and evolution and deviation analysis.
• A pattern evaluation module that works in tandem with the data mining modules by employing
interestingness measures to help focus the search towards interesting patterns.
• A graphical user interface that provides the user with an interactive approach to the data mining
system.
