Data Warehousing & Data Mining
ISBN: 9789351199120
Exclusively distributed by Technical Publication
Description
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
Introduction
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