Jean-Marc Adamo

Data Mining for Association Rules and Sequential Patterns (eBook, PDF)

Sequential and Parallel Algorithms

• Format: PDF

Produktbeschreibung

Recent advances in data collection, storage technologies, and computing power have made it possible for companies, government agencies and scientific laboratories to keep and manipulate vast amounts of data relating to their activities. This state-of-the-art monograph discusses essential algorithms for sophisticated data mining methods used with large-scale databases, focusing on two key topics: association rules and sequential pattern discovery. This will be an essential book for practitioners and professionals in computer science and computer engineering.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 254
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461300854
• Artikelnr.: 43987592

Inhaltsangabe

1. Introduction.- 2. Search Space Partition-Based Rule Mining.- 2.1 Problem Statement.- 2.2 Search Space.- 2.3 Splitting Procedure.- 2.4 Enumerating ?-Frequent Attribute Sets (cass).- 2.5 Sequential Enumeration Procedure.- 2.6 Parallel Enumeration Procedure.- 2.7 Generating the Association Rules.- 3. Apriori and Other Algorithms.- 3.1 Early Algorithms.- 3.2 The Apriori Algorithms.- 3.3 Direct Hashing and Pruning.- 3.4 Dynamic Set Counting.- 4. Mining for Rules over Attribute Taxonomies.- 4.1 Association Rules over Taxonomies.- 4.2 Problem Statement and Algorithms.- 4.3 Pruning Uninteresting Rules.- 5. Constraint-Based Rule Mining.- 5.1 Boolean Constraints.- 5.2 Prime Implicants.- 5.3 Problem Statement and Algorithms.- 6. Data Partition-Based Rule Mining.- 6.1 Data Partitioning.- 6.2 cas Enumeration with Partitioned Data.- 7. Mining for Rules with Categorical and Metric Attributes.- 7.1 Interval Systems and Quantitative Rules.- 7.2 k-Partial Completeness.- 7.3 Pruning Uninteresting Rules.- 7.4 Enumeration Algorithms.- 8. Optimizing Rules with Quantitative Attributes.- 8.1 Solving 1-1-Type Rule Optimization Problems.- 8.2 Solving d-1-Type Rule Optimization Problems.- 8.3 Solving 1-q-Type Rule Optimization Problems.- 8.4 Solving d-q-Type Rule Optimization Problems.- 9. Beyond Support-Confidence Framework.- 9.1 A Criticism of the Support-Confidence Framework.- 9.2 Conviction.- 9.3 Pruning Conviction-Based Rules.- 9.4 One-Step Association Rule Mining.- 9.6 Refining Conviction: Association Rule Intensity.- 10. Search Space Partition-Based Sequential Pattern Mining.- 10.1 Problem Statement.- 10.2 Search Space.- 10.3 Splitting the Search Space.- 10.4 Splitting Procedure.- 10.5 Sequence Enumeration.- Appendix 1. Chernoff Bounds.- Appendix 2. Partitioning in Figure 10.5: Beyond3rd Power.- Appendix 3. Partitioning in Figure 10.6: Beyond 3rd Power.- References.