跟上时代学点儿Big Data 知识---
海量数据集的挖掘
英文原版P341
得嘱咐两句，本书研究的可是“very large amounts of data, that is, data so large  it does not fit in main memory. Because of the emphasis on size, many of our  examples are about the Web or data derived from the Web.”，即海量数据集。
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 fit 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 file systems and map-reduce as a tool for creating parallel
algorithms that succeed on very large amounts of data.
2. Similarity search, including the key techniques of minhashing and localitysensitive
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-dimensional datasets.
7. Two key problems for Web applications: managing advertising and recommendation
systems.