All Chapters Covered
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SOLUTION MANUAL
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,Contents
1 Introduction 3
1.11 Exercises ............................................................................................................................................................... 3
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2 Data Preprocessing
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2.8 Exercises .............................................................................................................................................................. 13
3 Data Warehouse and OLAP Technology: An Overview
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3.7 Exercises .............................................................................................................................................................. 31
4 Data Cube Computation and Data Generalization
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4.5 Exercises .............................................................................................................................................................. 41
5 Mining Frequent Patterns, Associations, and Correlations
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5.7 Exercises .............................................................................................................................................................. 53
6 Classification and Prediction g f g f 69
6.17 Exercises ............................................................................................................................................................. 69
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7 Cluster Analysis g f 79
7.13 Exercises ............................................................................................................................................................. 79
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8 Mining Stream, Time-Series, and Sequence Data
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8.6 Exercises .............................................................................................................................................................. 91
9 Graph Mining, Social Network Analysis, and Multirelational Data Mining
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9.5 Exercises .............................................................................................................................................................103
10 Mining Object, Spatial, Multimedia, Text, and Web Data
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10.7 Exercises .............................................................................................................................................................111
11 Applications and Trends in Data Mining
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11.7 Exercises .............................................................................................................................................................123
1
,Chapter 1 g f
Introduction
1.11 Exercises
1.1. What is data mining? In your answer, address the following:
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(a) Is it another hype?
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(b) Is it a simple transformation of technology developed from databases, statistics, and machine learning?
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(c) Explain how the evolution of database technology led to data mining.
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(d) Describe the steps involved in data mining when viewed as a process of knowledge discovery.
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Answer:
Data mining refers to the process or method that extracts or “mines” interesting knowledge or pat
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terns from large amounts of data.
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(a) Is it another hype?
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Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availabil
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ity of huge amounts of data and the imminent need for turning such data into useful information and kno
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wledge. Thus, data mining can be viewed as the result of the natural evolution of information technolo
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gy.
(b) Is it a simple transformation of technology developed from databases, statistics, and machine learning?
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No. Data mining is more than a simple transformation of technology developed from databases, sta-
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tistics, and machine learning. Instead, data mining involves an integration, rather than a
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simple
transformation, of techniques from multiple disciplines such as database technology, statistics, ma-
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chine learning, high- gf gf
performance computing, pattern recognition, neural networks, data visualization, information retrieval,
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image and signal processing, and spatial data analysis.
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(c) Explain how the evolution of database technology led to data mining.
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Database technology began with the development of data collection and database creation mechanisms
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that led to the development of effective mechanisms for data management including data storage and
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retrieval, and query and transaction processing. The large number of database systems offering query a
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nd transaction processing eventually and naturally led to the need for data analysis and understanding. H
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ence, data mining began its development out of this necessity.
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(d) Describe the steps involved in data mining when viewed as a process of knowledge discovery.
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The steps involved in data mining when viewed as a process of knowledge discovery are as follows:
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• Data cleaning, a process that removes or transforms noise and inconsistent data
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• Data integration, where multiple data sources may be combined
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3
, 4 CHAPTER 1. gf g f g f INTRODUCTIO
N
• Data selection, where data relevant to the analysis task are retrieved from the database
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• Data transformation, where data are transformed or consolidated into forms appropriate f
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or mining gf
• Data mining, an essential process where intelligent and efficient methods are applied in order to
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extract patterns gf
• Pattern evaluation, a process that identifies the truly interesting patterns representing k
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nowl- edge based on some interestingness measures gf gf gf gf gf gf
• Knowledge presentation, where visualization and knowledge representation techniques are used
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to present the mined knowledge to the user
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1.2. Present an example where data mining is crucial to the success of a business. What data mining functions d
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oes this business need? Can they be performed alternatively by data query processing or simple statistical a
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nalysis?
Answer:
A department store, for example, can use data mining to assist with its target marketing mail campaign. Usin
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g data mining functions such as association, the store can use the mined strong association rules to determin
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e which products bought by one group of customers are likely to lead to the buying of certain other produ
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cts. With this information, the store can then mail marketing materials only to those kinds of customers wh
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o exhibit a high likelihood of purchasing additional products. Data query processing is used for data or
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information retrieval and does not have the means for finding association rules. Similarly, simple statistical
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analysis cannot handle large amounts of data such as those of customer records in a department store.
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1.3. Suppose your task as a software engineer at Big-
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University is to design a data mining system to examine their university course database, which contains the
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gffollowing information: the name, address, and status (e.g., undergraduate or graduate) of each student, th
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e courses taken, and their cumulative grade point average (GPA). Describe the architecture you would choos
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e. What is the purpose of each component of this architecture?
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Answer:
A data mining architecture that can be used for this application would consist of the following major co
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mponents:
• A database, data warehouse, or other information repository, which consists of the set of database
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s, data warehouses, spreadsheets, or other kinds of information repositories containing the student a
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nd course information.
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• A database or data warehouse server, which fetches the relevant data based on the users’ data mi
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ning requests. gf
• A knowledge base that contains the domain knowledge used to guide the search or to evaluate the inte
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restingness of resulting patterns. For example, the knowledge base may contain concept hierarchies and
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metadata (e.g., describing data from multiple heterogeneous sources).
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• A data mining engine, which consists of a set of functional modules for tasks such as classification, asso
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ciation, classification, cluster analysis, and evolution and deviation analysis.
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• A pattern evaluation module that works in tandem with the data mining modules by employing interest
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ingness measures to help focus the search towards interesting patterns.
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• A graphical user interface that provides the user with an interactive approach to the data mining s
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ystem.
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SOLUTION MANUAL
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,Contents
1 Introduction 3
1.11 Exercises ............................................................................................................................................................... 3
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2 Data Preprocessing
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2.8 Exercises .............................................................................................................................................................. 13
3 Data Warehouse and OLAP Technology: An Overview
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3.7 Exercises .............................................................................................................................................................. 31
4 Data Cube Computation and Data Generalization
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4.5 Exercises .............................................................................................................................................................. 41
5 Mining Frequent Patterns, Associations, and Correlations
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5.7 Exercises .............................................................................................................................................................. 53
6 Classification and Prediction g f g f 69
6.17 Exercises ............................................................................................................................................................. 69
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7 Cluster Analysis g f 79
7.13 Exercises ............................................................................................................................................................. 79
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8 Mining Stream, Time-Series, and Sequence Data
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8.6 Exercises .............................................................................................................................................................. 91
9 Graph Mining, Social Network Analysis, and Multirelational Data Mining
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9.5 Exercises .............................................................................................................................................................103
10 Mining Object, Spatial, Multimedia, Text, and Web Data
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10.7 Exercises .............................................................................................................................................................111
11 Applications and Trends in Data Mining
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11.7 Exercises .............................................................................................................................................................123
1
,Chapter 1 g f
Introduction
1.11 Exercises
1.1. What is data mining? In your answer, address the following:
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(a) Is it another hype?
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(b) Is it a simple transformation of technology developed from databases, statistics, and machine learning?
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(c) Explain how the evolution of database technology led to data mining.
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(d) Describe the steps involved in data mining when viewed as a process of knowledge discovery.
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Answer:
Data mining refers to the process or method that extracts or “mines” interesting knowledge or pat
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terns from large amounts of data.
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(a) Is it another hype?
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Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availabil
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ity of huge amounts of data and the imminent need for turning such data into useful information and kno
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wledge. Thus, data mining can be viewed as the result of the natural evolution of information technolo
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gy.
(b) Is it a simple transformation of technology developed from databases, statistics, and machine learning?
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No. Data mining is more than a simple transformation of technology developed from databases, sta-
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tistics, and machine learning. Instead, data mining involves an integration, rather than a
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simple
transformation, of techniques from multiple disciplines such as database technology, statistics, ma-
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chine learning, high- gf gf
performance computing, pattern recognition, neural networks, data visualization, information retrieval,
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image and signal processing, and spatial data analysis.
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(c) Explain how the evolution of database technology led to data mining.
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Database technology began with the development of data collection and database creation mechanisms
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that led to the development of effective mechanisms for data management including data storage and
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retrieval, and query and transaction processing. The large number of database systems offering query a
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nd transaction processing eventually and naturally led to the need for data analysis and understanding. H
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ence, data mining began its development out of this necessity.
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(d) Describe the steps involved in data mining when viewed as a process of knowledge discovery.
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The steps involved in data mining when viewed as a process of knowledge discovery are as follows:
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• Data cleaning, a process that removes or transforms noise and inconsistent data
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• Data integration, where multiple data sources may be combined
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3
, 4 CHAPTER 1. gf g f g f INTRODUCTIO
N
• Data selection, where data relevant to the analysis task are retrieved from the database
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• Data transformation, where data are transformed or consolidated into forms appropriate f
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or mining gf
• Data mining, an essential process where intelligent and efficient methods are applied in order to
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extract patterns gf
• Pattern evaluation, a process that identifies the truly interesting patterns representing k
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nowl- edge based on some interestingness measures gf gf gf gf gf gf
• Knowledge presentation, where visualization and knowledge representation techniques are used
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to present the mined knowledge to the user
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1.2. Present an example where data mining is crucial to the success of a business. What data mining functions d
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oes this business need? Can they be performed alternatively by data query processing or simple statistical a
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nalysis?
Answer:
A department store, for example, can use data mining to assist with its target marketing mail campaign. Usin
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g data mining functions such as association, the store can use the mined strong association rules to determin
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e which products bought by one group of customers are likely to lead to the buying of certain other produ
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cts. With this information, the store can then mail marketing materials only to those kinds of customers wh
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o exhibit a high likelihood of purchasing additional products. Data query processing is used for data or
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information retrieval and does not have the means for finding association rules. Similarly, simple statistical
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analysis cannot handle large amounts of data such as those of customer records in a department store.
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1.3. Suppose your task as a software engineer at Big-
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University is to design a data mining system to examine their university course database, which contains the
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gffollowing information: the name, address, and status (e.g., undergraduate or graduate) of each student, th
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e courses taken, and their cumulative grade point average (GPA). Describe the architecture you would choos
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e. What is the purpose of each component of this architecture?
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Answer:
A data mining architecture that can be used for this application would consist of the following major co
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mponents:
• A database, data warehouse, or other information repository, which consists of the set of database
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s, data warehouses, spreadsheets, or other kinds of information repositories containing the student a
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nd course information.
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• A database or data warehouse server, which fetches the relevant data based on the users’ data mi
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ning requests. gf
• A knowledge base that contains the domain knowledge used to guide the search or to evaluate the inte
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restingness of resulting patterns. For example, the knowledge base may contain concept hierarchies and
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metadata (e.g., describing data from multiple heterogeneous sources).
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• A data mining engine, which consists of a set of functional modules for tasks such as classification, asso
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ciation, classification, cluster analysis, and evolution and deviation analysis.
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• A pattern evaluation module that works in tandem with the data mining modules by employing interest
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ingness measures to help focus the search towards interesting patterns.
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• A graphical user interface that provides the user with an interactive approach to the data mining s
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ystem.
