Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms / Edition 1
by Jean-Marc Adamo
Recent advances in the data collection and storage technologies have m ade it possible for companies (e.g. bar-code technology), administrati ve agencies (e.g. census data) and scientific laboratories (e.g. molec ule databases in chemistry or biology) to keep vast amounts of data re lating to their activities. At the same time, the availability of chea p computing
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Recent advances in the data collection and storage technologies have m ade it possible for companies (e.g. bar-code technology), administrati ve agencies (e.g. census data) and scientific laboratories (e.g. molec ule databases in chemistry or biology) to keep vast amounts of data re lating to their activities. At the same time, the availability of chea p computing power has made automatic extraction of structured knowledg e from these data feasible. Such an activity is referred to as data mi ning. More recently, the advent on the marketplace of cheap high perfo rmance (gigabit level) communication switches is even placing cheap pa rallel data mining within the reach of the majority.
Product Details
- ISBN-13:
- 9780387950488
- Publisher:
- Springer New York
- Publication date:
- 12/28/2000
- Edition description:
- 2001
- Pages:
- 254
- Product dimensions:
- 9.21(w) x 6.14(h) x 0.63(d)
Table of Contents
Introduction.- Search Space Partition-Based Rule Mining.- Apriori and Other Algorithms.- Mining for Rules Over Attribute Taxonomies.- Constraint-Based Rule Mining.- Data Partition-Based Rule Mining.- Mining Rules with Categorical and Metric Attributes.- Optimizing Rules with Quantitative Attributes.- Beyond Support-Confidence Framework.- Sequential Patterns: Search Space Partition-Based Mining.- Appendix.- References.- Index.
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