Mining of Massive Datasets
| Author | : | |
| Rating | : | 4.70 (908 Votes) |
| Asin | : | 1107077230 |
| Format Type | : | paperback |
| Number of Pages | : | 476 Pages |
| Publish Date | : | 2016-03-06 |
| Language | : | English |
DESCRIPTION:
A wonderful reference book Sneha 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 ma. Yow-Bang (Darren) Wang said An assortment of heuristics and algorithms. 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;"An assortment of heuristics and algorithms" according to Yow-Bang (Darren) Wang. 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 . . 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 . Good Textbook at a Reasonable Price 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 goo
His investments include Facebook (one of the earliest angel investors in 2005), Aster Data Systems (acquired by Teradata), Efficient Frontier (acquired by Adobe), Neoteris (acquired by Juniper), Transformic (acquired by Google), and several others. Recent awards include the Knuth Prize (2000), and the Si
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'. Codd Innovations award (2006). In 2012, Fast Company magazine named Anand to its list of '100 Most Creative People in Business'. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. In 1996, Anand co-founded Junglee,
The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. 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