Data Mining and Business Intelligence
Content
Data Mining
and Business
Intelligence
Includes Practicals
THE BOOK COVERS
�
Introduction to Data
�
Mining
Frequent Pattern
Mining
�
Data Exploration
�
Business Intelligence
�
Data Preprocessing
�
Decision Support
�
Classification
�
Clustering
�
Outlier Analysis
System
�
BI and Data Mining
` 379 /-
Applications
ABOUT THE BOOK
ISBN: 9789351197188 • Pages: 380 • Authors: Shinde, Chandrasekhar
The book introduces the concept of data mining as an important tool for enterprise data management and as a cutting edge technology
for building competitive advantage. The readers will be able to effectively identify sources of data and process it for data mining and
become well versed in all data mining algorithms, methods, and tools. The coverage of the book will help you to analytically identify
opportunities to derive business value from data.
IN THIS BOOK, YOU WILL LEARN ABOUT
�
�
�
�
�
�
�
�
�
�
Introduction to Data Mining: Patterns of data mining; technologies used for data mining; major issues in data mining
Data Exploration: Types of attributes; statistical description of data; data visualization; measuring similarity and dissimilarity
Data Preprocessing: Need for preprocessing; data cleaning; data integration; data reduction; clustering and sampling; normalization; histogram
analysis
Classification: Methods of classification; decision tree induction; Bayesian classification; model evaluation and selection; combing classifiers
Clustering: Basic concepts of cluster analysis; partitioning methods; hierarchical methods; density-based methods
Outlier Analysis: Types of outliers and their methods such as supervised, semi-supervised, unsupervised, proximity-based, and clustering-based
Frequent Pattern Mining: Frequent itemsets; closed itemsets; association rules; frequent pattern mining; generating association rules from frequent
itemsets
Business Intelligence: Defining BI; BI architectures; Factors in BI project; Developing BI system
Decision Support System: Evolution of information systems; development of DSS; decision-making process
BI and Data Mining Applications: Various data mining techniques such as fraud detection, clickstream mining, market segmentation, and retail
industry
ABOUT THE AUTHORS
S. K. Shinde is a Professor at Lokmanya Tilak College of Engineering, Navi Mumbai. He received B. E. (Computer Engineering) in 1999 and
M. E. (Information Technology) in 2004 from university of Mumbai, India. He completed his Ph.D. (Computer Engineering) in October
-2012 from Swami Ramanand Teertha Marathwada University, India. Currently, he is working in the field of Programming; Web Mining;
Frequent Pattern Discovery; and Integration of domain knowledge, such as ontologies in the mining process and benefits of patterns in web
personalized recommendations for ebusiness. He has published more than 20 research papers in the international journals and conferences.
Uddagiri Chandrasekhar has over 9 years of experience in software industry and academics. He received his M.Tech degree from IIIT,
Allahabad in the year 2005 and is currently working as Assistant professor-senior at VIT University, Vellore. He has few International
publications and his active area of research is in Data Mining. DT Editorial Services has seized the market of engineering textbooks,
bringing excellent content in engineering and technical education to the fore. The team is committed to providing excellence in quality of
content by judiciously analyzing the needs of its readers and ensuring dedication of its authors and editors in catering.
/dtechpress
/dtechpress
/dreamtechpress
dreamtechpress.wordpress.com
/company/dreamtech-press
TABLE OF CONTENTS
1 Introduction to Data Mining
yy Definition of Data Mining
Techniques used for Data Mining
yy How Does Data Mining Work?
yy Architecture of Data Mining
yy Kinds of Data that can be Mined
yy Data Mining Functionalities
yy Types of Data Mining Systems
yy Advantages of Data Mining
yy Disadvantages of Data Mining
yy Ethical Issues in Data Mining
2 Data Exploration
yy Data
Bootstrap
Comparing Classifier Performance Using
ROC Curves
yy Combining Classifiers (Ensemble Methods)
Bagging
Boosting
Random Forests
...and more
5 Clustering
yy Introducing Cluster Analysis
...and more
/dtechpress
yy
yy
yy
yy
Business Intelligence Framework 2020
DB2 Framework for BI
Role of Mathematical Models in BI
Factors Responsible for a Successful BI Project
Development of BI System
Obstacles to Business Intelligence in an
Organization
Ethics and Business Intelligence
...and more
Requirements of a Good Clustering
yy
Algorithm
9 Decision Support System
Types of Data in Clustering
yy Clustering Methodologies
yy Concept of Decision Making
Partitioning Methods
Types of decisions
Hierarchical Methods
Decision-making process
Density-Based Clustering
...and more
yy Techniques of Decision Making
Types of Attributes of Data
Statistical Description of Data
yy Data Visualization
6 Outlier Analysis
Visualization Techniques
Measuring Similarity and Dissimilarity in
yy Real-World Applications
Data
...and more
yy Types of Outliers
yy Outlier Challenges
3 Data Preprocessing
Noise versus Outliers
yy Why Preprocessing?
Issues with Multivariate Outlier Detection
yy Data Cleaning
Issues with Multiple Outliers
Missing Values
Choice of Appropriate Model
Noisy Data
yy Outlier Detection Approaches
Data Cleaning as a Process
yy Outlier Detection Methods
yy Data Integration
Various Application Scenarios for Outlier
yy Data Reduction
Detection Methods
10
Data Cube Aggregation
yy Proximity-Based Outlier Analysis
Attribute Subset Selection
Distance-Based Approach
Dimensionality Reduction
Density-Based Clustering
Numerosity Reduction
yy Clustering-Based Outlier Analysis ...and more
yy Data Transformation
7 Frequent Pattern Mining
Normalization
yy Market Basket Analysis
yy Data Discretization and Concept Hierarchy
Generation
Frequent Itemsets, Closed Itemsets, and
Association Rules
Binning
Frequent Pattern Mining Technique
Histogram Analysis
...and more
yy Efficient and Scalable Frequent Itemset Mining
4 Classification
Methods
yy Basic Concepts
Apriori Algorithm for Finding Frequent
Data Preparation
Itemsets using Candidate Generation
Data Types
Generating Association Rules from
yy Classification Methods
Frequent Itemsets
Decision Tree Induction
Improving Efficiency of Apriori Algorithm
Decision Tree Algorithm
A Pattern Growth Approach for Mining
Frequent Itemsets
Bayesian Classification
Mining Frequent Itemsets Using VDFs
Other Classification Methods
Mining Closed and Maximal Patterns
yy Prediction
yy Mining Multilevel and Multidimensional
Structure of Regression Model
Association Rules
Simple Linear Regression
yy Association Mining to Correlation Analysis
Multiple Linear Regression (Multivariable
Pattern Evaluation Measures
Linear Regression)
Constraint-Based
Nonlinear Regression
Association Mining
...and more
yy Model Evaluation and Selection
8 Introduction to Business Intelligence
Accuracy and Error Measures
Holdout
yy Data, Information, and Knowledge
Random Sampling
yy Defining Business Intelligence
Cross-Validation
yy Important Factors in Business Intelligence
Published by:
yy Business Intelligence Architecture
yy Business Intelligence Framework
yy Understanding Decision Support System (DSS)
yy Evolution of Information System
yy 9.5Development of Decision Support System
DSS Development Issues
Decision-Oriented Diagnosis
Feasibility Study
Selection of a Development Approach
yy Application of DSS
yy Role of Business Intelligence in
Decision Making
...and more
BI and Data Mining Applications281
yy ERP and Business Intelligence
Implementation of an ERP System
yy BI Applications in CRM
Application of a Cost-Effective CRM System
yy BI Applications in Marketing
yy
yy
yy
yy
yy
yy
yy
yy
Marketing Models
Relationship Marketing
Sales Force Management
BI Applications in Logistics and Production
Logistics Model
Supply Chain Optimization
Optimization Models for Logistics Planning
Revenue Management Systems
Business Intelligence in Logistics and Supply
Chain Management
Role of BI in Finance
Meaning of Finance
BI Applications in Finance
Financial Reporting
Financial Planning
Financial Analysis
BI Applications in Banking
BI Applications in Telecommunications
BI Applications in Fraud Detection
BI Applications in Clickstream Mining
BI Applications in the Retail Industry
Practicals
...and more
DREAMTECH PRESS
WILEY INDIA PVT. LTD.
19-A, Ansari Road, Daryaganj
4435-36/7, Ansari Road, Daryaganj
New Delhi-110 002, INDIA
New Delhi-110 002, INDIA
Tel: +91-11-2324 3463-73, Fax: +91-11-2324 3078
Tel: +91-11-4363 0000, Fax: +91-11-2327 5895
Email: [email protected]
Email: [email protected]
Website: www.dreamtechpress.com
Website: www.wileyindia.com
Regional Offices: Bangalore: Tel: +91-80-2313 2383, Fax: +91-80-2312 4319, Email: [email protected]
Mumbai: Tel: +91-22-2788 9263, 2788 9272, Telefax: +91-22-2788 9263, Email: [email protected]
/dtechpress
/dreamtechpress
dreamtechpress.wordpress.com
Execlusively
Distributed by:
/company/dreamtech-press
and Business
Intelligence
Includes Practicals
THE BOOK COVERS
�
Introduction to Data
�
Mining
Frequent Pattern
Mining
�
Data Exploration
�
Business Intelligence
�
Data Preprocessing
�
Decision Support
�
Classification
�
Clustering
�
Outlier Analysis
System
�
BI and Data Mining
` 379 /-
Applications
ABOUT THE BOOK
ISBN: 9789351197188 • Pages: 380 • Authors: Shinde, Chandrasekhar
The book introduces the concept of data mining as an important tool for enterprise data management and as a cutting edge technology
for building competitive advantage. The readers will be able to effectively identify sources of data and process it for data mining and
become well versed in all data mining algorithms, methods, and tools. The coverage of the book will help you to analytically identify
opportunities to derive business value from data.
IN THIS BOOK, YOU WILL LEARN ABOUT
�
�
�
�
�
�
�
�
�
�
Introduction to Data Mining: Patterns of data mining; technologies used for data mining; major issues in data mining
Data Exploration: Types of attributes; statistical description of data; data visualization; measuring similarity and dissimilarity
Data Preprocessing: Need for preprocessing; data cleaning; data integration; data reduction; clustering and sampling; normalization; histogram
analysis
Classification: Methods of classification; decision tree induction; Bayesian classification; model evaluation and selection; combing classifiers
Clustering: Basic concepts of cluster analysis; partitioning methods; hierarchical methods; density-based methods
Outlier Analysis: Types of outliers and their methods such as supervised, semi-supervised, unsupervised, proximity-based, and clustering-based
Frequent Pattern Mining: Frequent itemsets; closed itemsets; association rules; frequent pattern mining; generating association rules from frequent
itemsets
Business Intelligence: Defining BI; BI architectures; Factors in BI project; Developing BI system
Decision Support System: Evolution of information systems; development of DSS; decision-making process
BI and Data Mining Applications: Various data mining techniques such as fraud detection, clickstream mining, market segmentation, and retail
industry
ABOUT THE AUTHORS
S. K. Shinde is a Professor at Lokmanya Tilak College of Engineering, Navi Mumbai. He received B. E. (Computer Engineering) in 1999 and
M. E. (Information Technology) in 2004 from university of Mumbai, India. He completed his Ph.D. (Computer Engineering) in October
-2012 from Swami Ramanand Teertha Marathwada University, India. Currently, he is working in the field of Programming; Web Mining;
Frequent Pattern Discovery; and Integration of domain knowledge, such as ontologies in the mining process and benefits of patterns in web
personalized recommendations for ebusiness. He has published more than 20 research papers in the international journals and conferences.
Uddagiri Chandrasekhar has over 9 years of experience in software industry and academics. He received his M.Tech degree from IIIT,
Allahabad in the year 2005 and is currently working as Assistant professor-senior at VIT University, Vellore. He has few International
publications and his active area of research is in Data Mining. DT Editorial Services has seized the market of engineering textbooks,
bringing excellent content in engineering and technical education to the fore. The team is committed to providing excellence in quality of
content by judiciously analyzing the needs of its readers and ensuring dedication of its authors and editors in catering.
/dtechpress
/dtechpress
/dreamtechpress
dreamtechpress.wordpress.com
/company/dreamtech-press
TABLE OF CONTENTS
1 Introduction to Data Mining
yy Definition of Data Mining
Techniques used for Data Mining
yy How Does Data Mining Work?
yy Architecture of Data Mining
yy Kinds of Data that can be Mined
yy Data Mining Functionalities
yy Types of Data Mining Systems
yy Advantages of Data Mining
yy Disadvantages of Data Mining
yy Ethical Issues in Data Mining
2 Data Exploration
yy Data
Bootstrap
Comparing Classifier Performance Using
ROC Curves
yy Combining Classifiers (Ensemble Methods)
Bagging
Boosting
Random Forests
...and more
5 Clustering
yy Introducing Cluster Analysis
...and more
/dtechpress
yy
yy
yy
yy
Business Intelligence Framework 2020
DB2 Framework for BI
Role of Mathematical Models in BI
Factors Responsible for a Successful BI Project
Development of BI System
Obstacles to Business Intelligence in an
Organization
Ethics and Business Intelligence
...and more
Requirements of a Good Clustering
yy
Algorithm
9 Decision Support System
Types of Data in Clustering
yy Clustering Methodologies
yy Concept of Decision Making
Partitioning Methods
Types of decisions
Hierarchical Methods
Decision-making process
Density-Based Clustering
...and more
yy Techniques of Decision Making
Types of Attributes of Data
Statistical Description of Data
yy Data Visualization
6 Outlier Analysis
Visualization Techniques
Measuring Similarity and Dissimilarity in
yy Real-World Applications
Data
...and more
yy Types of Outliers
yy Outlier Challenges
3 Data Preprocessing
Noise versus Outliers
yy Why Preprocessing?
Issues with Multivariate Outlier Detection
yy Data Cleaning
Issues with Multiple Outliers
Missing Values
Choice of Appropriate Model
Noisy Data
yy Outlier Detection Approaches
Data Cleaning as a Process
yy Outlier Detection Methods
yy Data Integration
Various Application Scenarios for Outlier
yy Data Reduction
Detection Methods
10
Data Cube Aggregation
yy Proximity-Based Outlier Analysis
Attribute Subset Selection
Distance-Based Approach
Dimensionality Reduction
Density-Based Clustering
Numerosity Reduction
yy Clustering-Based Outlier Analysis ...and more
yy Data Transformation
7 Frequent Pattern Mining
Normalization
yy Market Basket Analysis
yy Data Discretization and Concept Hierarchy
Generation
Frequent Itemsets, Closed Itemsets, and
Association Rules
Binning
Frequent Pattern Mining Technique
Histogram Analysis
...and more
yy Efficient and Scalable Frequent Itemset Mining
4 Classification
Methods
yy Basic Concepts
Apriori Algorithm for Finding Frequent
Data Preparation
Itemsets using Candidate Generation
Data Types
Generating Association Rules from
yy Classification Methods
Frequent Itemsets
Decision Tree Induction
Improving Efficiency of Apriori Algorithm
Decision Tree Algorithm
A Pattern Growth Approach for Mining
Frequent Itemsets
Bayesian Classification
Mining Frequent Itemsets Using VDFs
Other Classification Methods
Mining Closed and Maximal Patterns
yy Prediction
yy Mining Multilevel and Multidimensional
Structure of Regression Model
Association Rules
Simple Linear Regression
yy Association Mining to Correlation Analysis
Multiple Linear Regression (Multivariable
Pattern Evaluation Measures
Linear Regression)
Constraint-Based
Nonlinear Regression
Association Mining
...and more
yy Model Evaluation and Selection
8 Introduction to Business Intelligence
Accuracy and Error Measures
Holdout
yy Data, Information, and Knowledge
Random Sampling
yy Defining Business Intelligence
Cross-Validation
yy Important Factors in Business Intelligence
Published by:
yy Business Intelligence Architecture
yy Business Intelligence Framework
yy Understanding Decision Support System (DSS)
yy Evolution of Information System
yy 9.5Development of Decision Support System
DSS Development Issues
Decision-Oriented Diagnosis
Feasibility Study
Selection of a Development Approach
yy Application of DSS
yy Role of Business Intelligence in
Decision Making
...and more
BI and Data Mining Applications281
yy ERP and Business Intelligence
Implementation of an ERP System
yy BI Applications in CRM
Application of a Cost-Effective CRM System
yy BI Applications in Marketing
yy
yy
yy
yy
yy
yy
yy
yy
Marketing Models
Relationship Marketing
Sales Force Management
BI Applications in Logistics and Production
Logistics Model
Supply Chain Optimization
Optimization Models for Logistics Planning
Revenue Management Systems
Business Intelligence in Logistics and Supply
Chain Management
Role of BI in Finance
Meaning of Finance
BI Applications in Finance
Financial Reporting
Financial Planning
Financial Analysis
BI Applications in Banking
BI Applications in Telecommunications
BI Applications in Fraud Detection
BI Applications in Clickstream Mining
BI Applications in the Retail Industry
Practicals
...and more
DREAMTECH PRESS
WILEY INDIA PVT. LTD.
19-A, Ansari Road, Daryaganj
4435-36/7, Ansari Road, Daryaganj
New Delhi-110 002, INDIA
New Delhi-110 002, INDIA
Tel: +91-11-2324 3463-73, Fax: +91-11-2324 3078
Tel: +91-11-4363 0000, Fax: +91-11-2327 5895
Email: [email protected]
Email: [email protected]
Website: www.dreamtechpress.com
Website: www.wileyindia.com
Regional Offices: Bangalore: Tel: +91-80-2313 2383, Fax: +91-80-2312 4319, Email: [email protected]
Mumbai: Tel: +91-22-2788 9263, 2788 9272, Telefax: +91-22-2788 9263, Email: [email protected]
/dtechpress
/dreamtechpress
dreamtechpress.wordpress.com
Execlusively
Distributed by:
/company/dreamtech-press
