Frequent closed itemset based algorithms
| Autor: | Tarek Hamrouni, S. Ben Yahia, E. Mephu Nguifo |
|---|---|
| Přispěvatelé: | Chevallier, Francois, Centre de Recherche en Informatique de Lens (CRIL), Université d'Artois (UA)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2006 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
Focal point Association rule learning Computer science business.industry Geography Planning and Development Structural classification Machine learning computer.software_genre [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Side effect (computer science) Benchmark (computing) General Earth and Planetary Sciences Artificial intelligence Data mining business computer Algorithm Water Science and Technology |
| Zdroj: | SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, Association for Computing Machinery (ACM), 2006, 8 n°1, pp.93-104 |
| ISSN: | 1931-0153 1931-0145 |
| DOI: | 10.1145/1147234.1147248 |
| Popis: | As a side effect of the digitalization of unprecedented amount of data, traditional retrieval tools proved to be unable to extract hidden and valuable knowledge. Data Mining, with a clear promise to provide adequate tools and/or techniques to do so, is the discovery of hidden information that can be retrieved from datasets. In this paper, we present a structural and analytical survey of frequent closed itemset (FCI) based algorithms for mining association rules. Indeed, we provide a structural classification, in four categories, and a comparison of these algorithms based on criteria that we introduce. We also present an analytical comparison of FCI-based algorithms using benchmark dense and sparse datasets as well as "worst case" datasets. Aiming to stand beyond classical performance analysis, we intend to provide a focal point on performance analysis based on memory consumption and advantages and/or limitations of optimization strategies, used in the FCI-based algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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