Mining of Massive Datasets 2nd Edition by Jure Leskovec (PDF)

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 map-reduce 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 second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This book is a delight for anyone who deals with practical Data Mining applications. Over the past few years, I have gathered bits and pieces of knowledge from various sources about machine learning, Map Reduce programming paradigm, design and analysis of algorithms, information retrieval, etc. But this book serves to tie it all together beautifully. If you have delved in the above topics and are looking for a reference book that strikes a balance between rigor and practicality, this book will serve you right. On the other hand, if you are just starting out in the field of Data Mining/Machine Learning then you may do well by starting out with more detailed material.The book has a nice compilation of many “greatest hits” algorithms, especially those related to mining graph data. The book treats the theory and the implementation aspects of algorithms with equal importance with ample consideration for scaling.The examples in the book are very intuitive and the book follows an easy to understand train of thought. The chapter summaries are a pleasant surprise. They are a great resource to help you distill and digest the key points from each chapter. The summaries are succinct enough to be un-intimidating and are descriptive enough to be useful.The book does keep referring back and forth between chapters but that is only because much of the material is actually interlinked and treating the topics in isolation would miss the point.All in all a great purchase for a lifetime!

⭐If you hesitate to buy this book, I would suggest you go to the official site of the book and check it. Full official PDF is available on the MMDS site.I got this book to take Stanford MMDS online course but then decided to read it fully (the course does not cover some advanced topics). The book content is very accessible. For example in chapter 5 authors cover PageRank algorithm, instead of introducing it via probability and linear algebra (Markov chains and eigenvectors) they touch the theory a little and then provide many examples, so the book is very practical oriented. Knowledge in probability and linear algebra would help, but not necessary, although you still need to know some very basic concepts like matrix by matrix multiplication.The book covers a wide range of topics from MapReduce and Locality Sensitive Hashing (LSH) to algorithms on graphs and large scale machine learning. I think you would not regret the purchase.

⭐First, the book is affordable at under $70. That is a big deal. You can download a PDF for free at several sites, but printing it would cost you $70 and the physical package would not be nearly as good. This is a significant physical hardback book.Content, they cover a lot of topics.I like the way the chapters are arranged. There are summaries at the end of every chapter. I found myself reading the summaries of topics before reading the pertinent sections and then reading the summaries again section by section. I learned much more using that practice instead of simply reading cover to cover in order.This is a good book. It is a good substitute for any number of online learning programs in data science.

⭐As the textbook of the Stanford online course of same title, this books is an assortment of heuristics and algorithms from data mining to some big data applications nowadays. I think this book can be especially suitable for those who:1. Have some machine learning background and want to have a quick glance over every popular data mining techniques;2. Have learned data mining and need to quickly look up some phrases along with compact explanations.In other word, I don’t think this book is for those who wish to see rigorous mathematical elements because frankly the content far from that; also, if you’re totally new to machine learning or data mining, you can take your first step from here, but it’ll be a struggled step I would guess. However, if you’re buying this book to go with the online course, then this is a great complement.

⭐Excellent background and examples of data mining. Well written for a text book. A great reference guide to understand details, especially if you already know a bit about data mining.

⭐liked it for the depth of the topic in the book.

⭐very helpful information and easy to understand even for the new student

⭐The print is good.

⭐Excellent

⭐A good starting point, but I’d warn that it doesn’t provide the depth I expected it would have. Pretty much every chapter is a brief introduction into a field related to data mining, but if you are really interested in applying the techniques, you will have to browse through the references to find more specialized material.My one for any recommendation system researcher along with the online lectures. This is 2nd edition. 3rd one is about to release. wait for it in case you have time.

⭐Extensive, yet easy to follow introduction to a lot of techniques. The book also focuses a lot on cloud algorithms.

⭐This is not the review about the book, the books is fantastic! the paper back print that I received had terrible quality, very thin papers and you can see through them all the writings on the other side of the page! making it impossible to read this book!

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