Data Mining And Warehousing Techniques With Machine Learning Concepts (Hardcover)

P. Vijayakumar

ISBN 10: 9819803128 / ISBN 13: 9789819803125

Verlag: World Scientific Publishing Co Pte Ltd, Singapore, 2025

Beschreibung

Hardcover. This unique compendium elaborates the basic perceptions of data warehouses and data mining. The former part of the book covers concepts like introduction to data warehouses, the need for using such data warehouses and key terminologies used in this framework. The latter part of the book covers the data mining concepts and the data mining techniques used in various applications and also explains the machine learning techniques in detail with suitable examples wherever essential.The book is written in simple English and is user-friendly. Each chapter is modeled with several sample scenarios and illustrations wherever necessary. The complete contents of each chapter include chapter technical content, summary, key points to remember, and few case studies for class group discussions and problem-solving.This volume clearly benefits professionals, academicians, data analysts, machine-learning community, undergraduate and postgraduate students. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Bestandsnummer des Verkäufers 9789819803125

Bibliografische Details

• Titel: Data Mining And Warehousing Techniques With Machine Learning Concepts (Hardcover)
• Verlag: World Scientific Publishing Co Pte Ltd, Singapore
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-10: 9819803128
• ISBN-13: 9789819803125
• Einband: Hardcover
• Zustand: new

Über diesen Titel

Inhaltsangabe

This unique compendium elaborates the basic perceptions of data warehouses and data mining. The former part of the book covers concepts like introduction to data warehouses, the need for using such data warehouses and key terminologies used in this framework. The latter part of the book covers the data mining concepts and the data mining techniques used in various applications and also explains the machine learning techniques in detail with suitable examples wherever essential. The book is written in simple English and is user-friendly. Each chapter is modeled with several sample scenarios and illustrations wherever necessary. The complete contents of each chapter include chapter technical content, summary, key points to remember, and few case studies for class group discussions and problem-solving. This volume clearly benefits professionals, academicians, data analysts, machine-learning community, undergraduate and postgraduate students.