Data mining and data warehouse new edition

Here is an overview of the new edition of "Data Mining and Data Warehousing" by Raghu Ramakrishnan and Johannes Gehrke:

New Edition:

The new edition of "Data Mining and Data Warehousing" is the 4th edition, published in 2020. The book has been thoroughly updated to reflect the latest developments in the field of data mining and data warehousing.

Key Changes:

  1. New Chapter on Deep Learning: The new edition includes a new chapter on deep learning, which covers the basics of deep learning, its applications in data mining, and its limitations.
  2. Updated Coverage of Big Data: The book now includes more extensive coverage of big data, including Hadoop, Spark, and NoSQL databases.
  3. New Section on Explainable AI: The new edition includes a new section on explainable AI, which discusses the importance of interpretability in machine learning models.
  4. Updated Case Studies: The book includes updated case studies on data mining and data warehousing, including examples from industries such as healthcare, finance, and e-commerce.
  5. New Exercises and Projects: The new edition includes new exercises and projects to help students apply the concepts learned in the book to real-world problems.

Table of Contents:

The book is divided into 14 chapters, covering the following topics:

  1. Introduction to Data Mining and Data Warehousing
  2. Data Warehousing and OLAP
  3. Data Mining: An Overview
  4. Data Preprocessing
  5. Clustering
  6. Classification
  7. Regression
  8. Decision Trees and Rule Induction
  9. Association Rule Mining
  10. Text Mining
  11. Web Mining
  12. Deep Learning
  13. Explainable AI
  14. Case Studies in Data Mining and Data Warehousing

Target Audience:

The book is designed for students and professionals who want to learn about data mining and data warehousing. It is suitable for:

  1. Undergraduate and graduate students in computer science, information systems, and related fields.
  2. Professionals working in data science, business intelligence, and analytics.
  3. Anyone interested in learning about data mining and data warehousing.

Prerequisites:

The book assumes a basic understanding of computer science and programming concepts, such as data structures, algorithms, and database systems. No prior knowledge of data mining or data warehousing is required.

Format:

The book is available in print and digital formats, including e-book and online course materials.