Mining of massive datasets

Mining of massive datasets

http://infolab.stanford.edu/~ullman/mmds/book.pdf

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.