© 2001

Data Mining for Association Rules and Sequential Patterns

Sequential and Parallel Algorithms


Table of contents

  1. Front Matter
    Pages i-x
  2. Jean-Marc Adamo
    Pages 1-4
  3. Jean-Marc Adamo
    Pages 5-32
  4. Jean-Marc Adamo
    Pages 33-48
  5. Jean-Marc Adamo
    Pages 49-65
  6. Jean-Marc Adamo
    Pages 67-78
  7. Jean-Marc Adamo
    Pages 79-91
  8. Jean-Marc Adamo
    Pages 111-150
  9. Jean-Marc Adamo
    Pages 151-184
  10. Back Matter
    Pages 229-254

About this book


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.


combinatorial optimization complexity computational complexity computer science data mining database knowledge optimization proving

Authors and affiliations

  1. 1.Université de LyonVilleurbanne, cedexFrance

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