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 A priori, Pincer-search, Dynamic Itemset Counting, FP-Tree growth, SLIQ, SPRINT, BOAT, CART, RainForest, BIRCH, CURE, BUBBLE, ROCK, STIRR, PAM, CLARANS, DBSCAN, GSP, SPADE, SPIRIT, etc. Interesting and recent developments such as Support Vector Machines and Rough Set Theory are also covered in the book. The book also discusses the mining of web data, spatial data, temporal data and text data. This book can serve as a textbook for students of computer science, mathematical science and management science. It can also be an excellent handbook for researchers in the area of data mining and data warehousing. The revised edition includes a comprehensive chapter on rough set theory. The rough set theory, which is a tool of sets and relations for studying imprecision, vagueness, and uncertainty in data analysis, is a relatively new mathematical and artificial intelligence technique. The discussion on association rule mining has been extended to include rapid association rule mining (RARM), FP-Tree Growth Algorithm for discovering association rule and the Eclat and dEclat algorithms. These appear in Chapter 4.
FOREWORD PROLOGUE PREFACE TO 2ND EDITION PREFACE TO IST EDITION ACKNOWLEDGEMENT 1 INTRODUCTION 1.1 Introduction 1.2 Data Mining as a Subject 1.3 Guide to this Book 2 DATA WAREHOUSING 2.1 Introduction 2.2 What is a Data Warehouse? 2.3 Definition 2.4 Multidimensional Data Model 2.5 OLAP Operations 2.6 Warehouse Schema 2.7 Data Warehousing Architecture 2.8 Warehouse Server 2.9 Metadata 2.10 OLAP Engine 2.11 Data Warehouse Backend Process 2.12 Other Features 2.13 Summary Exercises Bibliography 3 DATA MINING 3.1 Introduction 3.2 What is Data Mining? 3.3 Data Mining: Definitions 3.4 KDD vs. Data Mining 3.5 DBMS vs. DM 3.6 Other Related Areas 3.7 DM Techniques 3.8 Other Mining Problems 3.9 Issues and Challenges in DM 3.10 DM Application Areas 3.11 DM Applications Case Studies 3.12 Conclusion Further Reading Exercises Bibliography 4 ASSOCIATION RULES 4.1 Introduction 4.2 What is an Association Rule? 4.3 Methods to Discover Association Rules 4.4 A Priori Algorithm 4.5 Partition Algorithm 4.6 Pincer-Search Algorithm 4.7 Dynamic Itemset Counting Algorithm 4.8 FP-tree Growth Algorithm 4.9 Eclat and dEclat 4.10 Rapid Association Rule Mining 4.11 Discussion on Different Algorithms 4.12 Incremental Algorithm 4.13 Border Algorithm 4.14 Generalized Association Rule 4.15 Association Rules with Item Constraints 4.16 Summary Further Reading Exercises Bibliography 5 CLUSTERING TECHNIQUES 5.1 Introduction 5.2 Clustering Paradigms 5.3 Partitioning Algorithms 5.4 k-Medoid Algorithms 5.5 CLARA 5.6 CLARANS 5.7 Hierarchical Clustering 5.8 DBSCAN 5.9 BIRCH 5.10 CURE 5.11 Categorical Clustering Algorithms 5.12 STIRR 5.13 ROCK 5.14 CACTUS 5.15 Conclusion Further Reading Exercises Bibliography 6 DECISION TREES 6.1 Introduction 6.2 What is a Decision Tree? 6.3 Tree Construction Principle 6.4 Best Split 6.5 Splitting Indices 6.6 Splitting Criteria 6.7 Decision Tree Construction Algorithms 6.8 CART 6.9 ID3 6.10 C4.5 6.11 CHAID 6.12 Summary 6.13 Decision Tree Construction with Presorting 6.14 Rain Forest 6.15 Approximate Methods 6.16 CLOUDS 6.17 BOAT 6.18 Pruning Technique 6.19 Integration of Pruning and Construction 6.20 Summary: An Ideal Algorithm 6.21 Other Topics 6.22 Conclusion Further Reading Exercises Bibliography 7 ROUGH SET THEORY 7.1 Introduction 7.2 Definition 7.3 Example 7.4 Reduct 7.5 Propositional Reasoning and PIAP to Compute Reducts 7.6 Types of Reducts 7.7 Rule Extraction 7.8 Decision T
Author Bio (3900 characters maximum): Arun K Pujari is Professor of Computer Science at the University of Hyderabad, Hyderabad. Prior to joining the university, he served at the Automated Cartography Cell, Survey of India, Dehradun, and Jawaharlal Nehru University, New Delhi. He received his PhD from the Indian Institute of Technology 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. Professor Pujari is at present the vice-chancellor of Sambalpur University.
This book was added to South Asia bookstore on Wednesday 18 January, 2012.