Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms

✍ Scribed by Jean-Marc Adamo (auth.)

• Publisher: Springer-Verlag New York
• Year: 2001
• Tongue: English
• Leaves: 259
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
Introduction....Pages 1-4
Search Space Partition-Based Rule Mining....Pages 5-32
Apriori and Other Algorithms....Pages 33-48
Mining for Rules over Attribute Taxonomies....Pages 49-65
Constraint-Based Rule Mining....Pages 67-78
Data Partition-Based Rule Mining....Pages 79-91
Mining for Rules with Categorical and Metric Attributes....Pages 93-109
Optimizing Rules with Quantitative Attributes....Pages 111-150
Beyond Support-Confidence Framework....Pages 151-184
Search Space Partition-Based Sequential Pattern Mining....Pages 185-228
Back Matter....Pages 229-254

✦ Subjects

Database Management; Information Storage and Retrieval; Artificial Intelligence (incl. Robotics); Algorithm Analysis and Problem Complexity