Data Mining and Business Intelligence

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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.
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TABLE OF CONTENTS
1 Introduction to Data Mining
yy Definition of Data Mining

ŒŒ 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

ŒŒ Bootstrap
ŒŒ Comparing Classifier Performance Using
ROC Curves
yy Combining Classifiers (Ensemble Methods)
ŒŒ Bagging
ŒŒ Boosting
ŒŒ Random Forests
...and more

5 Clustering
yy Introducing Cluster Analysis
...and more

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yy
yy
yy
yy

ŒŒ Business Intelligence Framework 2020
ŒŒ 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

ŒŒ Requirements of a Good Clustering
yy
Algorithm
9 Decision Support System
ŒŒ Types of Data in Clustering
yy Clustering Methodologies
yy Concept of Decision Making
ŒŒ Partitioning Methods
ŒŒ Types of decisions
ŒŒ Hierarchical Methods
ŒŒ Decision-making process
ŒŒ Density-Based Clustering
...and more
yy Techniques of Decision Making

ŒŒ Types of Attributes of Data
ŒŒ Statistical Description of Data
yy Data Visualization
6 Outlier Analysis
ŒŒ Visualization Techniques
ŒŒ Measuring Similarity and Dissimilarity in
yy Real-World Applications
Data
...and more
yy Types of Outliers
yy Outlier Challenges
3 Data Preprocessing
ŒŒ Noise versus Outliers
yy Why Preprocessing?
ŒŒ Issues with Multivariate Outlier Detection
yy Data Cleaning
ŒŒ Issues with Multiple Outliers
ŒŒ Missing Values
ŒŒ Choice of Appropriate Model
ŒŒ Noisy Data
yy Outlier Detection Approaches
ŒŒ Data Cleaning as a Process
yy Outlier Detection Methods
yy Data Integration
ŒŒ Various Application Scenarios for Outlier
yy Data Reduction
Detection Methods
10
ŒŒ Data Cube Aggregation
yy Proximity-Based Outlier Analysis
ŒŒ Attribute Subset Selection
ŒŒ Distance-Based Approach
ŒŒ Dimensionality Reduction
ŒŒ Density-Based Clustering
ŒŒ Numerosity Reduction
yy Clustering-Based Outlier Analysis ...and more
yy Data Transformation
7 Frequent Pattern Mining
ŒŒ Normalization
yy Market Basket Analysis
yy Data Discretization and Concept Hierarchy
Generation
ŒŒ Frequent Itemsets, Closed Itemsets, and
Association Rules
ŒŒ Binning
ŒŒ Frequent Pattern Mining Technique
ŒŒ Histogram Analysis
...and more
yy Efficient and Scalable Frequent Itemset Mining
4 Classification
Methods
yy Basic Concepts
ŒŒ Apriori Algorithm for Finding Frequent
ŒŒ Data Preparation
Itemsets using Candidate Generation
ŒŒ Data Types
ŒŒ Generating Association Rules from
yy Classification Methods
Frequent Itemsets
ŒŒ Decision Tree Induction
ŒŒ Improving Efficiency of Apriori Algorithm
ŒŒ Decision Tree Algorithm
ŒŒ A Pattern Growth Approach for Mining
Frequent Itemsets
ŒŒ Bayesian Classification
ŒŒ Mining Frequent Itemsets Using VDFs
ŒŒ Other Classification Methods
ŒŒ Mining Closed and Maximal Patterns
yy Prediction
yy Mining Multilevel and Multidimensional
ŒŒ Structure of Regression Model
Association Rules
ŒŒ Simple Linear Regression
yy Association Mining to Correlation Analysis
ŒŒ Multiple Linear Regression (Multivariable
ŒŒ Pattern Evaluation Measures
Linear Regression)
ŒŒ Constraint-Based
ŒŒ Nonlinear Regression
Association Mining
...and more
yy Model Evaluation and Selection
8 Introduction to Business Intelligence
ŒŒ Accuracy and Error Measures
ŒŒ Holdout
yy Data, Information, and Knowledge
ŒŒ Random Sampling
yy Defining Business Intelligence
ŒŒ 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
ŒŒ DSS Development Issues
ŒŒ Decision-Oriented Diagnosis
ŒŒ Feasibility Study
ŒŒ 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
ŒŒ Implementation of an ERP System

yy BI Applications in CRM

ŒŒ Application of a Cost-Effective CRM System

yy BI Applications in Marketing

yy

yy

yy
yy
yy
yy
yy
yy

ŒŒ Marketing Models
ŒŒ Relationship Marketing
ŒŒ Sales Force Management
BI Applications in Logistics and Production
ŒŒ Logistics Model
ŒŒ Supply Chain Optimization
ŒŒ Optimization Models for Logistics Planning
ŒŒ Revenue Management Systems
ŒŒ Business Intelligence in Logistics and Supply
Chain Management
Role of BI in Finance
ŒŒ Meaning of Finance
ŒŒ BI Applications in Finance
ŒŒ Financial Reporting
ŒŒ Financial Planning
ŒŒ 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

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