Mining Of Massive Datasets, 3rd Edition (South Asia Edition)

Description

Buy Mining Of Massive Datasets, 3rd Edition (South Asia Edition) 2nd hand books online, Authored by jure leskovec in paperback binding from Used Book Store.

This used copy of Mining of Massive Datasets, 3rd Edition (South Asia Edition) is written in English. Modern applications, from the web and social media to mobile activity and sensors, generate vast datasets from which valuable information can be extracted through data mining. This book concentrates on practical algorithms adept at solving key data mining challenges and scalable to the largest datasets. The text commences with an exploration of the MapReduce framework and complementary techniques for effective parallel programming. It delves into the principles of locality-sensitive hashing, a crucial body of knowledge for identifying similar objects within immense collections without the need for pairwise comparisons. Stream-processing algorithms for handling data that arrives too rapidly for complete processing are also presented. The PageRank concept and related methods for organizing the web are subsequently discussed. Further chapters address the challenges of discovering frequent itemsets and performing clustering, approached from the perspective that datasets exceed main memory capacity. Two vital e-commerce applications, recommendation systems and web advertising, are examined in depth. The later sections of the book cover algorithms for social network graph analysis, large-scale data compression, and machine learning. The third edition features expanded content on decision trees, deep learning, and mining social network graphs. Authored by leading experts in database and web technologies, this book is an essential resource for both students and professionals.

About the Author

Jure Leskovec, a leading figure in machine learning and data science, brings his expertise to this work from his position as an Associate Professor at Stanford University and Chief Scientist at Pinterest. His research, focused on applying machine learning to vast biomedical, social, and information networks, has profound implications across computer science, e-commerce, social sciences, and biomedicine. Dr. Leskovec’s background includes a Ph.D. from Carnegie Mellon University and prior postdoctoral work at Cornell University. Anand Rajaraman is a renowned serial entrepreneur and venture capitalist with a distinguished track record in founding and investing in transformative technology companies. As a Founding Partner at Rocketship VC, he leverages data mining and machine learning to identify promising global startups. His investment portfolio includes industry giants like Facebook and Lyft, and he co-founded successful ventures such as Junglee (acquired by Amazon.com) and Kosmix (acquired by Walmart). Following the Kosmix acquisition, he played a key role in establishing WalmartLabs. Rajaraman’s academic contributions have been recognized with multiple prestigious Best Paper awards, and he is a co-inventor of Amazon Mechanical Turk, a pioneer in crowdsourcing and human-machine computation. Jeffrey David Ullman is the Stanford W. Ascherman Professor of Computer Science (Emeritus) and the current CEO of Gradiance. A foundational figure in database theory, he has mentored a generation of leading database theorists. His research interests encompass database theory, data mining, and educational technology. Ullman is a recipient of numerous accolades, including the 2016 NEC C&C Foundation Prize and the 2010 IEEE John von Neumann Medal. His seminal contributions have shaped the fields of automata theory, language theory, and theoretical computer science.

9781009566872 ISBN
Computer Science
Computing, Internet & Digital Media