E-Book Details:
Title:
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Encyclopedia of data warehousing and mining, Volume 1
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Publisher:
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Idea group Reference
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Author:
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John Wang
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Edition:
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2nd, illustrated volume-1
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Format:
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PDF
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ISBN:
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1591405572,
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EAN:
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9781591405573
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No.ofPages:
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1280
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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.
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