Encyclopedia of data warehousing and mining, Volume 1
Idea group Reference
2nd, illustrated volume-1
ABOUT THE AUTHOR:
John Wang is a full professor in the Department of Management & Information Systems at Montclair State University. Having received a scholarship award, he came to the USA and completed his PhD in operations research at Temple University (1990). He has published over 100 refereed papers and four books. He has also developed several computer software programs based on his research findings. He is the Editor-in-Chief of the International Journal of Information Systems and Supply Chain Management. Also, he is the Editor of the Encyclopedia of Data Warehousing and Mining, 1st and 2nd Edition. He is on the editorial board of the International Journal of Cases on Electronic Commerce and has been a guest editor and referee for Operations Research, IEEE Transactions on Control Systems Technology, and many other highly prestigious journals. His long-term research goal is on the synergy of operations research, data mining and cybernetics.
Table of Contents:
UNIT - I
Introduction : Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues in Data Mining. Data Preprocessing : Needs Preprocessing the Data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation.
UNIT – II
Data Warehouse and OLAP Technology for Data Mining Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Implementation,Further Development of Data Cube Technology, From Data Warehousing to Data Mining.
UNIT - III
Data Mining Primitives, Languages, and System Architectures : Data Mining Primitives, Data Mining
Query Languages, Designing Graphical User Interfaces Based on a Data Mining Query Language
Architectures of Data Mining Systems.
UNIT - IV
Concepts Description : Characterization and Comparison : Data Generalization and Summarization-
Based Characterization, Analytical Characterization: Analysis of Attribute Relevance, Mining Class
Comparisons: Discriminating between Different Classes, Mining Descriptive Statistical Measures in Large Databases.
UNIT - V
Mining Association Rules in Large Databases : Association Rule Mining, Mining Single-Dimensional
Boolean Association Rules from Transactional Databases, Mining Multilevel Association Rules from
Transaction Databases, Mining Multidimensional Association Rules from Relational Databases and Data Warehouses, From Association Mining to Correlation Analysis, Constraint-Based Association Mining.
UNIT - VI
Classification and Prediction : Issues Regarding Classification and Prediction, Classification by
Decision Tree Induction, Bayesian Classification, Classification by Backpropagation, Classification Based on Concepts from Association Rule Mining, Other Classification Methods, Prediction, Classifier Accuracy.
UNIT - VII
Cluster Analysis Introduction : Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Outlier Analysis.
UNIT - VIII
Mining Complex Types of Data : Multimensional Analysis and Descriptive Mining of Complex, Data
Objects, Mining Spatial Databases, Mining Multimedia Databases, Mining Time-Series and Sequence Data, Mining Text Databases, Mining the World Wide Web.