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    72 Stanford University

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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 iii

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    Logistics Lectures are on Tuesday Thursday 3 00 4 20pm PST in NVIDIA Auditorium Lecture Videos are available on Canvas for all the enrolled Stanford students You can also check our past Coursera MOOC Public resources The lecture slides and assignments will be posted online as the course progresses We are happy for anyone to use these resources but we cannot grade the work of any

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    Mining of Massive Datasets 9781107015357 Computer

    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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    Mining of Massive Datasets Cambridge University Press

    Mining of Massive Datasets Second Edition 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

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    Mining of Massive Datasets Beta Version of Third Edition

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    SD201 Mining of Massive Datasets Fall 2017 Mauro Sozio

    10 10 The text and the datasets for the TP session on 11 10 have been posted We also posted the text for the TD on 18 10 We recommend you to solve those exercises which will be solved in class by one of the teaching assistants on 18 10

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    CS246 Mining Massive Datasets Homework 1

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    Mining Massive Data Sets Stanford Online

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    Mining of Massive Datasets Anand Rajaraman Jeffrey

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    Mining of Massive Datasets

    Preface This book evolved from material developed over several years by Anand Raja raman and Jeff Ullman for a one quarter course at Stanford The course CS345A titled Web Mining was designed as an advanced graduate course although it has become

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