Cart Download
   
Browse Catalogues


Data Mining Techniques
 
Arun K Pujari
Price : ₹ 395.00
ISBN : 978-81-7371-884-7
Language : English
Pages : 388
Binding : Paperback
Book Size : 180 x 240 mm
Year : 2013
Series :
Territorial Rights : World
Imprint : No Image
 
 
About the Book

Data Mining Techniques addresses all the major and latest techniques of data mining and data warehousing. It deals in detail with the latest algorithms for discovering association rules, decision trees, clustering, neural networks and genetic algorithms. The book contains the algorithmic details of different techniques such as Apriori, Pincer-search, Dynamic Itemset Counting, FP-Tree growth, SLIQ, SPRINT, BOAT, CART, RainForest, BIRCH, CURE, BUBBLE, ROCK, STIRR, PAM, CLARANS, DBSCAN, GSP, SPADE and SPIRIT. Interesting and recent developments such as support vector machines and rough set theory are also covered. The book also discusses the mining of web data, spatial data, temporal data and text data. The inclusion of well thought out illustrated examples for making the concepts clear to a first time reader makes the book suitable as a textbook for students of computer science, mathematical science and management science. It can also serve as a handbook for researchers in the area of data mining and data warehousing.

In this edition, the chapter on data warehousing has been thoroughly revised and its scope of coverage expanded to include a detailed discussion on multidimensional data modelling and cube computation. The discussion on genetic algorithms too has been considerably expanded to bring to fore its applications  in the context of data mining.

Table of Contents

Foreword
Prologue
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Acknowledgements

INTRODUCTION

Introduction
Data Mining as a Subject
Guide to this Book

DATA WAREHOUSING

Introduction
Data Warehouse Architecture
Dimensional Modelling
Categorisation of Hierarchies 2.5 Aggregate Function
Summarisability
Fact–Dimension Relationships
OLAP Operations
Lattice of Cuboids
OLAP Server
ROLAP
MOLAP
Cube Computation
Multiway Simultaneous Aggregation (ArrayCube)
BUC - Bottom-Up Cubing Algorithm
Condensed Cube
Coalescing
Dwarf
Other Cubing Techniques
Skycube
View Selection - Partial Materialisation
Data Marting
ETL
Data Cleaning
ELT vs. ETL
Cloud Data Warehousing Further Reading
Exercises
Bibliography

DATA MINING

Introduction
What is Data Mining?
Data Mining: Definitions
KDD vs. Data Mining 
DBMS vs. DM 
Other Related Areas 
DM Techniques 
Other Mining Problems 
Issues and Challenges in DM 
DM Application Areas 
DM Applications—Case Studies 
Conclusions 
Further Reading 
Exercises 
Bibliography 

ASSOCIATION RULES  

Introduction 
What is an Association Rule? 
Methods to Discover Association Rules 
Apriori Algorithm 
Partition Algorithm 
Pincer-Search Algorithm 
Dynamic Itemset Counting Algorithm 
FP-tree Growth Algorithm 
Eclat and dEclat 
Rapid Association Rule Mining (RARM) 
Discussion on Different Algorithms 
Incremental Algorithm 
Border Algorithm 
Generalised Association Rule
Association Rules with Item Constraints 
Summary 
Further Reading 
Exercises 
Bibliography 

CLUSTERING TECHNIQUES  

Introduction 
Clustering Paradigms 
Partitioning Algorithms 
k-Medoid Algorithms 
CLARA 
CLARANS 
Hierarchical Clustering 
DBSCAN 
BIRCH 
CURE 
Categorical Clustering Algorithms 
STIRR 
ROCK 
CACTUS 
Conclusions 
Further Reading 
Exercises 
Bibliography 

DECISION TREES  

Introduction 
What is a Decision Tree? 
Tree Construction Principle 
Best Split 
Splitting Indices 
Splitting Criteria 
Decision Tree Construction Algorithms 
CART 
ID3 
C4.5 
CHAID 
Summary 
Decision Tree Construction with Presorting 
RainForest 
Approximate Methods 
CLOUDS 
BOAT 
Pruning Technique 
Integration of Pruning and Construction 
Summary: An Ideal Algorithm 
Other Topics 
Conclusions 
Further Reading 
Exercises 
Bibliography 

ROUGH SET THEORY  

Introduction 
Definitions 
Example 
Reduct 
Propositional Reasoning and PIAP to Compute Reducts 
Types of Reducts 
Rule Extraction 
Decision tree 
Rough Sets and Fuzzy Sets 
Granular Computing 
Further Reading 
Exercises 
Bibiliography 

GENETIC ALGORITHM  

Introduction 
Basic Steps of GA 
Selection 
Crossover 
Mutation 
Data Mining Using GA 
GA for Rule Discovery 
GA and Decision Tree 
Clustering Using GA 
Conclusions 
Further Reading 
Exercises 
Bibliography 

OTHER TECHNIQUES  

Introduction 
What is a Neural Network? 
Learning in NN 
Unsupervised Learning 
Data Mining Using NN: A Case Study 
Support Vector Machines 
Conclusions 
Further Reading 
Exercises 
Bibliography 

WEB MINING  

Introduction 
Web Mining 
Web Content Mining 
Web Structure Mining 
Web Usage Mining 
Text Mining 
Unstructured Text 
Episode Rule Discovery for Texts 
Hierarchy of Categories 
Text Clustering 
Conclusions 
Further Reading 
Exercises 
Bibliography 

TEMPORAL AND SPATIAL DATA MINING  

Introduction 
What is Temporal Data Mining? 
Temporal Association Rules 
Sequence Mining 
The GSP Algorithm 
SPADE 
SPIRIT 
WUM 
Episode Discovery 
Event Prediction Problem 
Time-series Analysis 
Spatial Mining 
Spatial Mining Tasks 
Spatial Clustering 
Spatial Trends 
Conclusions 
Further Reading 
Exercises 
Bibliography 

Index

Contributors (Author(s), Editor(s), Translator(s), Illustrator(s) etc.)

Arun K Pujari is a professor of computer science at the University of Hyderabad, Hyderabad. Prior to joining the university, he served at Automated Cartography Cell, Survey of India, Dehradun, and Jawaharlal Nehru University, New Delhi. He received his PhD from IIT Kanpur and MSc from Sambalpur University, Sambalpur. He has also been on visiting ssignments to the Institute of Industrial Sciences, University of Tokyo; International Institute of Software Technology, United Nations University, Macau; University of Memphis, USA; and Griffith University, Australia, among others. He served as the vice-chancellor of Sambalpur University from November 2008 to November 2011.

Home | About Us | Our Associates | Publish with Us |  Our Network | Contact Us
Copyright © Orient BlackSwan, All rights reserved. See Disclaimer and Privacy Policy, Terms and Conditions   Frequently Asked Questions Bookmark and Share