Torrent details for "Deborah L. Data Mining And Warehousing Techniques With ML Learni…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
18.1 MB
Info Hash:
639B0C53C44673092EC359E8A9B32CE28FF5645D
Added By:
Added:
Oct. 25, 2025, 10:33 a.m.
Stats:
|
(Last updated: Oct. 25, 2025, 10:34 a.m.)
| File | Size |
|---|---|
| Deborah L. Data Mining And Warehousing Techniques With Machine Learning Concepts 2025.pdf | 18.1 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Deborah L. Data Mining And Warehousing Techniques With ML Learning Concepts 2025
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format 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 later 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. About the Authors. Introduction. Basics of Data Operations. Data Warehousing Basic Concepts. Data Warehouse Architecture. Designing Data Warehouses. Partitioning and Parallelism in Data Warehouses. Overview of Data Mining. Knowledge Representation and Knowledge Discovery. Data Mining Techniques. Machine Learning Using Classification. Machine Learning Using Clustering. Association Rules. Index
×