Mining of massive datasets
Jurij Leskovec (Author), Anand Rajaraman (Author), Jeffrey D. Ullman (Author)
This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to 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. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction. It includes a range of over 150 challenging exercises. -- Edited sumamry from book
eBook, English, 2014
2nd edition View all formats and editions
Cambridge University Press, Cambridge, 2014
1 online resource (xii, 467 pages) : illustrations
9781316147313, 9781139924801, 9781316147047, 9781107077232, 1316147312, 113992480X, 1316147045, 1107077230
888463433
Print version:
Data mining
MapReduce and the new software stack
Finding similar items
Mining data streams
Link analysis
Frequent itemsets
Clustering
Advertising on the Web
Recommendation systems
Mining social-network graphs
Dimensionality reduction
Large-scale machine learning
Previous edition: 2012
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