Data Mining for Association Rules and Sequential Patterns
Subtitle: Sequential and Parallel Algorithms
By: by Jean-Marc Adamo
Publisher: Springer New York; Springer
Published: 2001
Place of publication: New York, NY
Language: eng
Format: [electronic resource] :
Number of pages: 254
ISBN-13: 9781461265115; 9781461300854
Subjects: Information storage and retrieval systems; Computer software; Computer science; Artificial intelligence; Database management
Description: 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.
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Table of contents: title: Introduction; title: Search Space Partition-Based Rule Mining; title: Apriori and Other Algorithms; title: Mining for Rules Over Attribute Taxonomies; title: Constraint-Based Rule Mining; title: Data Partition-Based Rule Mining; title: Mining Rules with Categorical and Metric Attributes; title: Optimizing Rules with Quantitative Attributes; title: Beyond Support-Confidence Framework; title: Sequential Patterns: Search Space Partition-Based Mining; title: Appendix; title: References; title: Index.