Cover of: Data Mining for Association Rules and Sequential Patterns | Jean-Marc Adamo
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Data Mining for Association Rules and Sequential Patterns

Sequential and Parallel Algorithms

  • 7 Want to read

Published by Springer .
Written in English

About the Book

The book provides a unified presentation of algorithms for association rule and sequential pattern discovery. For both mining problems, the presentation relies on the lattice structure of the search space. All algorithms are built as processes running on this structure. Proving their properties takes advantage of the mathematical properties of the structure. Mining for association rules and sequential patterns is known to be a problem with large computational complexity. The issue of designing efficient parallel algorithms should be considered as critical. Most algorithms in the book are devised for both sequential and parallel execution. Parallel algorithm design takes advantage of the lattice structure of the search space. Partitioning is performed via lattice recursive bisection. Database partitioning is also used as an additional source of parallelism. Part of the motivation for writing this book was postgraduate teaching. Since the book only assumes elementary mathematical knowledge in the domains of lattices, combinatorial optimization, probability calculus, and statistics, it is fit for use by undergraduate students as well. The algorithms are described in a C-like pseudo programming language. The computations are shown in great detail. This makes the book also fit for use by implementers: computer scientists in many domains as well as industry engineers.

ID Numbers

Open Library
OL7448689M
ISBN 10
0387950486
ISBN 13
9780387950488
Library Thing
294076
Goodreads
7269576
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August 5, 2010 Edited by IdentifierBot added LibraryThing ID
April 24, 2010 Edited by Open Library Bot Fixed duplicate goodreads IDs.
April 16, 2010 Edited by bgimpertBot Added goodreads ID.
April 14, 2010 Edited by Open Library Bot Linked existing covers to the edition.
April 29, 2008 Created by an anonymous user Imported from amazon.com record.