Data Mining for Association Rules and Sequential Patterns

1. Front Matter

   Pages i-x

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2. Introduction

   • Jean-Marc Adamo

   Pages 1-4

3. Search Space Partition-Based Rule Mining

   • Jean-Marc Adamo

   Pages 5-32

4. Apriori and Other Algorithms

   • Jean-Marc Adamo

   Pages 33-48

5. Mining for Rules over Attribute Taxonomies

   • Jean-Marc Adamo

   Pages 49-65

6. Constraint-Based Rule Mining

   • Jean-Marc Adamo

   Pages 67-78

7. Data Partition-Based Rule Mining

   • Jean-Marc Adamo

   Pages 79-91

8. Mining for Rules with Categorical and Metric Attributes

   • Jean-Marc Adamo

   Pages 93-109

9. Optimizing Rules with Quantitative Attributes

   • Jean-Marc Adamo

   Pages 111-150

10. Beyond Support-Confidence Framework

    • Jean-Marc Adamo

    Pages 151-184

11. Search Space Partition-Based Sequential Pattern Mining

    • Jean-Marc Adamo

    Pages 185-228

12. Back Matter

    Pages 229-254

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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
• algorithm analysis and problem complexity

• Université de Lyon, Villeurbanne, cedex, France

  Jean-Marc Adamo

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