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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.
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Data Warehouse Architecture
Categorisation of Hierarchies 2.5 Aggregate Function
Lattice of Cuboids
Multiway Simultaneous Aggregation (ArrayCube)
BUC - Bottom-Up Cubing Algorithm
Other Cubing Techniques
View Selection - Partial Materialisation
ELT vs. ETL
Cloud Data Warehousing Further Reading
What is Data Mining?
Data Mining: Definitions
KDD vs. Data Mining
DBMS vs. DM
Other Related Areas
Other Mining Problems
Issues and Challenges in DM
DM Application Areas
DM Applications—Case Studies
What is an Association Rule?
Methods to Discover Association Rules
Dynamic Itemset Counting Algorithm
FP-tree Growth Algorithm
Eclat and dEclat
Rapid Association Rule Mining (RARM)
Discussion on Different Algorithms
Generalised Association Rule
Association Rules with Item Constraints
Categorical Clustering Algorithms
What is a Decision Tree?
Tree Construction Principle
Decision Tree Construction Algorithms
Decision Tree Construction with Presorting
Integration of Pruning and Construction
Summary: An Ideal Algorithm
ROUGH SET THEORY
Propositional Reasoning and PIAP to Compute Reducts
Types of Reducts
Rough Sets and Fuzzy Sets
Basic Steps of GA
Data Mining Using GA
GA for Rule Discovery
GA and Decision Tree
Clustering Using GA
What is a Neural Network?
Learning in NN
Data Mining Using NN: A Case Study
Support Vector Machines
TEMPORAL AND SPATIAL DATA MINING
What is Temporal Data Mining?
Temporal Association Rules
The GSP Algorithm
Event Prediction Problem
Spatial Mining Tasks
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.