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Data mining for association rules and sequential patterns

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Data mining for association rules and sequential patterns

Data mining for association rules and sequential patterns

Sequential and parallel algrithms

Jean-Marc Adamo

255 pages, parution le 15/01/2001

Résumé

Data mining-the automatic extraction of structured knowledge from large data sets-has become increasingly feasible and valuable with the greater availability of affordable and fast computing power. Recent technological advances in data collection and storage have made it possible for compagnies, administrative agencies, and scientific laboratories to keep vast amounts of data. Mining that data includes such activities as classification, clustering, similarity analysis, summarization, and association rule and sequential pattern discovery.

Data Mining for Association Rules and Sequential Patterns focuses on two i key areas of data mining. The book presents a collection of algorithm based on the lattice structure of the search space; all algorithms are built as processes running on this structure. Given the computational complexity and time requirements of mining for association rules and sequential patterns, the design of efficient algorithms is critical. Most algorithms provided here are designed for both sequential and parallel execution. In addition to enumerative algorithms, the book presents algorithms related to quantitative rule optimization. The algorithms are described in a C-like pseudoprogramming language and are supported by detailed computations.

Topics and features:

-offers a unified presentation of the main topics relating to association rule mining and sequential pattern mining -reviews all important algorithms proposed in the literature and presents new (until now unpublished) algorithms -utilizes mathematics for accurate algorithm development -provides solutions to the problem of parallel mining for association rules and sequential patterns -presents search-space and database partitioning techniques for parallel rule and sequential pattern mining

This unique, state-of-the-art monograph describes key algorithms used in the sophisticated data mining of large-scale databases. Practitioners and professionals in information science, computer science' database design, and software engineering will find the work an essential resource, as will teachers, students, and researchers involved in the domains of knowledge discovery, data mining, and data management.

Contents

  • Preface
  • Introduction
  • Search Space Partition-Based Rule Mining
  • Apriori and Other Algorithms
  • Minig for Rules over Attribute Taxonomies
  • Constraint -Based Rule Minig
  • Data Partition-Based Rule Minig
  • Mining for Rules with Catégorical and Metric Attributes
  • Optimizing Rules With Quantitative Attributes
  • Beyond Support-Confidence Framework
  • Search Space Partition-Based Sequential Pattern Mining
  • Appendix 1. Chernoff Bounds
  • Appendix 2. Partitioning in Figure 10.5 Beyond 3 rd Power
  • Appendix 3. Partitioning in Figure 10.6 Beyond 3 rd Power
  • References
  • Index

Caractéristiques techniques

  PAPIER
Éditeur(s) Springer
Auteur(s) Jean-Marc Adamo
Parution 15/01/2001
Nb. de pages 255
Format 15,9 x 24,2
Couverture Relié
Poids 523g
Intérieur Noir et Blanc
EAN13 9780387950488

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