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Data algorithms / Mahmoud Parsian.

By: Material type: TextTextPublication details: Beijing : O'Reily ; c. 2015.Edition: 1st edDescription: xxxvii, 737 p. : ill. ; 23 cmOther title:
  • Data algorithms : recipes for scaling up with Hadoop and Spark [Cover title]
Subject(s): DDC classification:
  • 004 22 PAR
Contents:
Secondary sort : introduction -- Secondary sort : a detailed example -- Top 10 list -- Left outer join -- Order inversion -- Moving average -- Market basket analysis -- Common friends -- Recommendation engines using MapReduce -- Content-based recommendation : movies -- Smarter email marketing with the Markov Model -- K-means clustering -- k-nearest neighbors -- Naive bayes -- Sentiment analysis -- Finding, counting, and listing all triangles in large graphs -- K-mer counting -- DNA sequencing -- Cox regression -- Cochran-Armitage test for trend -- Allelic frequency -- The T-test -- Pearson correlation -- DNA base count -- RNA sequencing -- Gene aggregation -- Linear regression -- MapReduce and monoids -- The small files problem -- Huge cache for MapReduce -- The bloom filter.
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Holdings
Item type Current library Collection Call number Vol info Status Date due Barcode Item holds
Book - Borrowing Book - Borrowing Central Library Lower Floor Academic Bookshop 004 PAR (Browse shelf(Opens below)) 9128 Available 000034460
Total holds: 0

Includes bibliographic references (page 721-723) and index.

Secondary sort : introduction -- Secondary sort : a detailed example -- Top 10 list -- Left outer join -- Order inversion -- Moving average -- Market basket analysis -- Common friends -- Recommendation engines using MapReduce -- Content-based recommendation : movies -- Smarter email marketing with the Markov Model -- K-means clustering -- k-nearest neighbors -- Naive bayes -- Sentiment analysis -- Finding, counting, and listing all triangles in large graphs -- K-mer counting -- DNA sequencing -- Cox regression -- Cochran-Armitage test for trend -- Allelic frequency -- The T-test -- Pearson correlation -- DNA base count -- RNA sequencing -- Gene aggregation -- Linear regression -- MapReduce and monoids -- The small files problem -- Huge cache for MapReduce -- The bloom filter.

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