文档介绍：Mining of Massive Datasets Anand Rajaraman Kosmix, Inc. Je?rey D. Ullman Stanford Univ. Copyright c2010, 2011 Anand Rajaraman and Je?rey D. Ullman ii Preface This book evolved from material developed over several years by Anand Raja- raman and Je? Ullman for a one-quarter course at Stanford. The course CS345A, titled “Web Mining,” was designed as an advanced graduate course, although it has e accessible and interesting to advanced undergraduates. What the Book Is About At the highest level of description, this book is about data mining. However, it focuses on data mining of very large amounts of data, that is, data so large it does not ?t in main memory. Because of the emphasis on size,many of our examples are about the Web or data derived from the Web. Further, the book takes an algorithmic point of view: data mining is about applying algorithms to data, rather than using data to “train” a machine-learning engine of some sort. The principal topics covered are: 1. Distributed ?le systems and map-reduce as a tool for creating parallel algorithms that eed on very large amounts of data. 2. Similarity search, including the key techniques of minhashing and locality- sensitive hashing. 3. Data-stream processing and specialized algorithms for dealing with data that arrives so fast it must be processed immediately or lost. 4. The technology of search engines, including Google’s PageRank, link-spam detection, and the hubs-and-authorities approach. 5. Frequent-itemset mining, including association rules,market-baskets, the A-Priori Algorithm and its improvements. 6. Algorithms for clustering very large, high-dimensionaldatasets. 7. Two key problems for Web applications: managing advertising and rec- ommendation systems. iii iv PREFACE Prerequisites CS345A, although its number indicates an advanced graduatecourse, has been found accessible by advanced undergraduates and beginningmasters students. In the future, it is likely that the course will be given a mezzanine-level numb