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. 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.

Web和Internet商务的普及提供了许多超大数据集，通过数据挖掘可以从中收集信息。本书重点介绍用于解决数据挖掘关键问题的实用算法，这些算法甚至可以用于最大的数据集。它首先讨论map-reduce框架，这是自动并行算法的一个重要工具。作者解释了局部敏感散列和流处理算法的技巧，用于挖掘到达太快而无法进行穷举处理的数据。