Mining of Massive Datasets van Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman is een Engelstalig gedrukt boek. Deze titel is geschikt voor studenten in het hoger onderwijs en professionals.
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.
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.
Now in its third edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Inhoudelijk sluit dit boek aan bij onderwerpen als Data mining, Pattern recognition, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition.