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Mining of massive datasets

By: Contributor(s): Material type: TextTextLanguage: English Publication details: New Delhi : Cambridge University Press, 2014.Edition: 2nd EdDescription: xii, 467 p. : illustrations 24 cmISBN:
  • 9781316638491
Subject(s): DDC classification:
  • 006.312 LES
Contents:
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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode
Reference Books Reference Books CUTN Central Library Reference Reference 006.312 LES (Browse shelf(Opens below)) Not For Loan 29646
General Books General Books CUTN Central Library Generalia Non-fiction 006.312 LES (Browse shelf(Opens below)) Available 29647
General Books General Books CUTN Central Library Generalia Non-fiction 006.312 LES (Browse shelf(Opens below)) Available 29648
General Books General Books CUTN Central Library Generalia Non-fiction 006.312 LES (Browse shelf(Opens below)) Available 29649
General Books General Books CUTN Central Library Generalia Non-fiction 006.312 LES (Browse shelf(Opens below)) Available 29650

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

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.







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