Data Warehousing & Data Mining

Varsha Bhosale, Deepali Vora

ISBN: 9789351199120

Exclusively distributed by Technical Publication 


This book, Data Warehousing and Mining, is a one-time reference that covers all aspects of data warehousing and mining in an easy-tounderstand manner. It covers a variety of topics, such as data warehousing and its benefits; architecture of data warehouse; data mart, data warehousing design strategies, dimensional modeling and features of a good dimensional model; different types of schemas such as star schema, snowflake schema; fact tables and dimension tables; concept of primary key, surrogate keys and foreign keys; ETL process; different types of data extraction such as immediate data extraction and deferred data extraction; Online Analytical Processing (OLAP) and need for online analytical processing etc



1 Data Warehousing

  • Data Warehouse
  • Components of a Data Warehouse
  • Building a Data Warehouse
  • Mapping Data Warehouse to a Multiprocessor Architecture
  • DBMS Schemas for Decision Support
  • Data Extraction, Clean up and Transformation Tools
  • Change Data Capture
  • Ways of Extracting Data


  2 Business Analysis

  • The Importance of Tools
  • Taxonomy of Data Warehouse Tools
  • Commercial Tools
  • Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP)
  • Multidimensional Data Modeling
  • OLAP Operations
  • OLAP Guidelines
  • Multidimensional versus Multi-relational OLAP OLAP Tools
  • OLAP Tools and the Internet


3 Data Mining

  • Data
  • Data Mining
  • Data Mining Functionalities
  • Interestingness Measures
  • Classification of Data Mining Systems
  • Data Mining Task Primitives
  • Integration of a Data Mining System with a  Data Warehouse
  • Issues in Data Mining
  • Data Preprocessing


4 Association Rule Mining and Classification

  • Market Basket Analysis
  • Efficient and Scalable Frequent Pattern Mining Methods
  • Multilevel and Multidimensional Association Rules
  • Association Rule Mining to Correlation Analysis
  • Constraint-based Association Mining
  • Classification and Prediction
  • Bayesian Classification
  • Classification by Artificial Neural Networks (Backpropagation)
  • Lazy learners (learning from Neighbors)
  • Support Vector Machine (SVM)
  • Associative Classification
  • Other Classification Methods
  • Prediction
  • Model Evaluation and Selection
  • Combining Classifiers (Ensemble Methods)


5 Clustering and Trends in Data Mining

  • Cluster Analysis
  • Types of Data in Clustering
  • Categorization of Major Clustering Methods
  • Partitioning Methods
  • Hierarchical Methods
  • Density-Based Clustering
  • Grid-based Methods
  • Model-based Clustering Methods
  • Clustering High Dimensional Data
  • Constraint-based Cluster Analysis
  • Outlier Analysis
  • Data Mining Applications




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