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 techniques such as locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank algorithm and related methods for organizing the Web, frequent itemset mining, clustering, recommendation systems, and advertising on the web. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Written by leading authorities in database and Web technologies, it is essential reading for students and practitioners alike. The book contains more than 250 exercises and provides slides, homework assignments, project requirements, and exams available online.

Key topics include data mining fundamentals, scalable algorithms for large datasets, graph mining, machine learning, and neural nets with deep learning