Mining of Massive Datasets 3rd Edition by Jure Leskovec (Author)

Mining of Massive Datasets 3rd Edition by Jure Leskovec (Author), Anand Rajaraman (Author), Jeffrey David Ullman (Author)

Mining of Massive Datasets (3rd Edition) by Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman provides a comprehensive guide to the techniques used in mining large-scale datasets. This edition focuses on the principles and applications of data mining, offering a deeper dive into algorithms and data structures necessary for handling vast amounts of data effectively.

Key Features:

1. Foundations of Data Mining

   • Introduction to basic concepts and algorithms in data mining, including clustering, classification, and regression.

2. Scalable Techniques for Large Datasets

   • Emphasizes algorithms designed to process large-scale datasets, from graph-based techniques to sampling and approximation methods.

3. Big Data Tools

   • Exploration of popular tools and frameworks like Hadoop, MapReduce, and Spark for handling massive datasets.

4. Real-World Applications

   • Case studies from diverse industries, such as social networks, recommender systems, and search engines.

5. Graph Mining

   • Detailed discussion on techniques for mining graph data, essential for understanding networks, social media, and large-scale data structures.

6. Mining Text and Web Data

   • In-depth coverage of text mining, web mining, and analyzing unstructured data using techniques such as natural language processing.

7. Advanced Topics

   • Coverage of cutting-edge topics like deep learning, streaming data, and distributed systems for data mining.

Conclusion:

This book is an essential resource for anyone interested in data mining, particularly those dealing with massive datasets. It provides a thorough foundation while also exploring advanced methods for real-world applications, making it suitable for students, professionals, and researchers in fields like data science, machine learning, and artificial intelligence.