- The proceedings of the challenge have been published in JMLR Workshop and Conference Proceedings.
- The datasets are available under the webscope program. (this one does not require approval and is available to anyone)
Showing posts with label Data Mining. Show all posts
Showing posts with label Data Mining. Show all posts
Wednesday, March 2, 2011
Mining of Massive Datasets
Anand Rajaraman and Jeff Ullman wrote a book called "Mining of Massive Datasets" that can be downloaded for free:
It focuses on data mining of very large amounts of data.
You can find materials from past offerings of CS345A at:
http://infolab.stanford.edu/~ullman/mining/mining.html
There, you will find slides, homework assignments, project requirements, and in some cases, exams.
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 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-dimensional datasets.
7. Two key problems for Web applications: managing advertising and recommendation systems.
It focuses on data mining of very large amounts of data.
You can find materials from past offerings of CS345A at:
http://infolab.stanford.edu/~ullman/mining/mining.html
There, you will find slides, homework assignments, project requirements, and in some cases, exams.
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 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-dimensional datasets.
7. Two key problems for Web applications: managing advertising and recommendation systems.
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