
Data Mining, Data Warehousing and OLAP
- Author: Gajendra Sharma
- Publisher: S.K. Kataria & Sons
- Binding: Paperback
- Number of Pages: 375
- Publication Date: 01-01-2013
- ISBN-13: 9788189757472
- Dimensions: 20.3 x 25.4 x 4.7 cm
- Language: English
Table of Contents:
-
Unit-I: Data Mining
- Data Processing
- Data Reduction
-
Unit-II: Data Mining Statistics
- Association Rules
-
Unit-III: Classification
- Decision Trees
- Bayesian Classification
- Artificial Neural Networks
- Genetic Algorithm
- Clustering
-
Unit-IV: Introduction to Data Warehousing
- Data Warehousing: Concepts and Mechanisms
- Building a Data Warehouse
- Client Server Computing
- Distributed and Parallel Processing
- Mapping Data Warehouse to a Multiprocessor Architecture
- Aggregation
- OLAP
- Business Competitive Analysis
- Security
-
Unit-V: Advanced Data Mining
- Web Mining
- Visual Web Data Mining
- Temporal and Spatial Data Mining
- Appendices
- References
- Examination Paper
- Index
Description:
Data Mining and Data Warehousing by Gajendra Sharma, published by S.K. Kataria & Sons, is a comprehensive 375-page guide focused on the intricate fields of data mining and data warehousing. With its clear structure and in-depth exploration, the book provides a thorough understanding of the various techniques and methodologies used in data processing, classification, and warehousing.
The book is divided into five main units, covering fundamental topics such as data mining statistics, classification algorithms, data warehousing concepts, and advanced topics like web mining and temporal/spatial data mining. The content is designed for both students and professionals seeking to master these essential areas of data science.
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Description
- Author: Gajendra Sharma
- Publisher: S.K. Kataria & Sons
- Binding: Paperback
- Number of Pages: 375
- Publication Date: 01-01-2013
- ISBN-13: 9788189757472
- Dimensions: 20.3 x 25.4 x 4.7 cm
- Language: English
Table of Contents:
-
Unit-I: Data Mining
- Data Processing
- Data Reduction
-
Unit-II: Data Mining Statistics
- Association Rules
-
Unit-III: Classification
- Decision Trees
- Bayesian Classification
- Artificial Neural Networks
- Genetic Algorithm
- Clustering
-
Unit-IV: Introduction to Data Warehousing
- Data Warehousing: Concepts and Mechanisms
- Building a Data Warehouse
- Client Server Computing
- Distributed and Parallel Processing
- Mapping Data Warehouse to a Multiprocessor Architecture
- Aggregation
- OLAP
- Business Competitive Analysis
- Security
-
Unit-V: Advanced Data Mining
- Web Mining
- Visual Web Data Mining
- Temporal and Spatial Data Mining
- Appendices
- References
- Examination Paper
- Index
Description:
Data Mining and Data Warehousing by Gajendra Sharma, published by S.K. Kataria & Sons, is a comprehensive 375-page guide focused on the intricate fields of data mining and data warehousing. With its clear structure and in-depth exploration, the book provides a thorough understanding of the various techniques and methodologies used in data processing, classification, and warehousing.
The book is divided into five main units, covering fundamental topics such as data mining statistics, classification algorithms, data warehousing concepts, and advanced topics like web mining and temporal/spatial data mining. The content is designed for both students and professionals seeking to master these essential areas of data science.


















