Mining of Massive Datasets by Jure Leskovec (English) Hardcover Book

Description: Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Publisher Description Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Author Biography Jure Leskovec is Associate Professor of Computer Science at Stanford University, California. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large-scale data, the Web, and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, an Okawa Foundation Fellowship, and numerous best paper awards. His research has also been featured in popular press outlets such as the New York Times, the Wall Street Journal, the Washington Post, MIT Technology Review, NBC, BBC, CBC, and Wired. Leskovec has authored the Stanford Network Analysis Platform (SNAP, , a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes and billions of edges. He is also Investigator at the Chan Zuckerberg Biohub. You can follow him on Twitter at @jure. Anand Rajaraman is a serial entrepreneur, venture capitalist, and academic based in Silicon Valley. He is a Founding Partner at Rocketship VC, an innovative venture capital firm that uses data mining and machine learning to find promising startup investments all over the world. Rajaramans investments include Facebook (one of the earliest angel investors in 2005), Lyft, Aster Data Systems (acquired by Teradata), Efficient Frontier (acquired by Adobe), Neoteris (acquired by Juniper), Transformic (acquired by Google), and several others. Rajaraman was, until recently, Senior Vice President at Walmart Global eCommerce and co-head of @WalmartLabs, where he worked at the intersection of social, mobile, and commerce. He came to Walmart when Walmart acquired Kosmix, the startup he co-founded, in 2011. Kosmix pioneered semantic search technology and semantic analysis of social media. In 1996, Rajaraman co-founded Junglee, an e-commerce pioneer. As Chief Technology Officer, he played a key role in developing Junglees award-winning Virtual Database technology. In 1998, Amazon.com acquired Junglee, and Rajaraman helped launch the transformation of Amazon.com from a retailer into a retail platform, enabling third-party retailers to sell on Amazon.coms website. He is also a co-inventor of Amazon Mechanical Turk, which pioneered the concepts of crowdsourcing and hybrid Human-Machine computation. As an academic, his research has focused at the intersection of database systems, the Web, and social media. His research publications have won several awards at prestigious academic conferences, including two retrospective 10-year Best Paper awards at ACM SIGMOD and VLDB. In 2012, Fast Company magazine named Rajaraman in its list of 100 Most Creative People in Business. In 2013, he was named a Distinguished Alumnus by his alma mater, IIT Madras. In addition to acting as a consulting assistant professor in the Computer Science Department at Stanford University, California, he is a spe Jeffrey David Ullman is the Stanford W. Ascherman Professor of Computer Science (Emeritus) and the current CEO of Gradiance. His research interests include database theory, data mining, and education using the information infrastructure. He is one of the founders of the field of database theory, and was the doctoral advisor of an entire generation of students who later became leading database theorists in their own right. He was the Ph.D. advisor of Sergey Brin, one of the co-founders of Google, and served on Googles technical advisory board. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, and he has held Guggenheim and Einstein Fellowships. He has received awards including the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006),and the 2016 NEC C&C Foundation Prize (with Al Aho and John Hopcroft). Ullman is also the co-recipient (with John Hopcroft) of the 2010 IEEE John von Neumann Medal, for laying the foundations for the fields of automata and language theory and many seminal contributions to theoretical computer science. Details ISBN 1108476341 ISBN-13 9781108476348 Title Mining of Massive Datasets Author Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman Format Hardcover Year 2020 Pages 565 Edition 3rd Publisher Cambridge University Press GE_Item_ID:126832316; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. 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