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 · Mining of Massive Datasets 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 used on even the largest datasets.

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 · Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman. 2014 | ISBN: 1107077230 | English | 476 pages | EPUB / PDF | 3 , 6MB. 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 ...

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 · A revised discussion of the relationship between data mining, machine learning, and statistics in Section 1.1. 2: Ch. 2: Spark and TensorFlow added to Section 2.4 on workflow systems: 3: Ch. 3: More efficient method for minhashing in Section 3.3: 10: Ch. 10

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A common sort of data-mining problem involves discovering unusual events hidden within massive amounts of data. This section is a discussion of the problem, including "Bonferroni's Principle," a warning against overzealous use of data mining.

Mining of Massive Datasets - Stanford University

 · Mining of Massive Datasets Jure Leskovec Stanford Univ. Anand Rajaraman Milliway Labs Jeffrey D. Ullman Stanford Univ. ... CS345A, titled "Web Mining," was designed as an advanced graduate course, although it has become accessible and interesting to advanced undergraduates.

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Mining of Massive DataSets - A Review. December 10, 2014 in courses. If you've been keeping track, I've hardly published a single article in the last month. In my defense, the last 7 months have been extremely hectic. High work pressure and a rather dense …

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 · Mining of Massive Data Sets. Author: Jure Leskovec. Publisher: Cambridge University Press. ISBN: Category: Business & Economics. Page: 565. View: 703. DOWNLOAD NOW ». Now in its third edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Mining Massive Datasets

 · This course will introduce basic and advanced techniques for massive datasets processing. Topics include: data mining basics, cloud computing platforms, programming models and MapReduce, large scale machine learning and data mining algorithms, and data-intensive applications. The goal of this course is to help students understand and exploit ...

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Mining of Massive Datasets . 2011. Abstract. 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 which can be used on even the largest datasets.

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 · Discover Mining of Massive Datasets, 3rd Edition, Jure Leskovec, HB ISBN: 9781108476348 on Higher Education from Cambridge

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Stanford CS246: Mining Massive Data Sets (Winter 2018)

Books: Leskovec-Rajaraman-Ullman: Mining of Massive Datasets can be downloaded for free. It can also be purchased from Cambridge University Press, but you are not required to do so. MOOC: You can watch videos from a past Coursera MOOC (similar to this course) on Youtube. Piazza: Piazza Discussion Group for this class.

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 · 5. Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements. 6. Algorithms for clustering very large, high-dimensional datasets. 7. Two key problems for Web applications: managing advertising and rec-ommendation systems. Table of Contents. Data Mining; Large-Scale File Systems and Map-Reduce

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Mining of Massive Datasets

 · CS341 Project in Mining Massive Data Sets is an advanced project based course. Students work on data mining and machine learning algorithms for analyzing very large amounts of data. Both interesting big datasets as well as computational infrastructure (large …

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 · Mining of Massive Datasets,The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining).The book, like the course, is designed at the undergraduate computer science level with no formal ...