Jean-Marc Adamo

Data Mining for Association Rules and Sequential Patterns

Sequential and Parallel Algorithms

• Gebundenes Buch

Produktbeschreibung

Data mining includes a wide range of activities such as classification, clustering, similarity analysis, summarization, association rule and sequential pattern discovery, and so forth. The book focuses on the last two previously listed activities. It 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. Part of the motivation for writing this book was postgraduate teaching. One of the main intentions was to make the book a suitable support for the clear exposition of problems and algorithms as well as a sound base for further discussion and investigation. 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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Produktdetails

• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-0-387-95048-8
• 2001
• Seitenzahl: 254
• Erscheinungstermin: 28. Dezember 2000
• Englisch
• Abmessung: 245mm x 164mm x 19mm
• Gewicht: 524g
• ISBN-13: 9780387950488
• ISBN-10: 0387950486
• Artikelnr.: 09614047

Inhaltsangabe

Introduction. - Search Space Partition-Based Rule Mining. - Apriori and Other Algorithms. - Mining for Rules Over Attribute Taxonomies. - Constraint-Based Rule Mining. - Data Partition-Based Rule Mining. - Mining Rules with Categorical and Metric Attributes. - Optimizing Rules with Quantitative Attributes. - Beyond Support-Confidence Framework. - Sequential Patterns: Search Space Partition-Based Mining. - Appendix. - References. - Index.