bitSPADE: A Lattice-Based Sequential Pattern Mining Algorithm Using Bitmap Representation
| Autor: | Aomar Osmani, Emmanuel Viennet, Sujeevan Aseervatham |
|---|---|
| Přispěvatelé: | Aseervatham, Sujeevan, Laboratoire d'Informatique de Paris-Nord (LIPN), Université Paris 13 (UP13)-Institut Galilée-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2006 |
| Předmět: |
Association rule learning
Computer science 02 engineering and technology computer.file_format [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] computer.software_genre [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 020204 information systems Lattice (order) 0202 electrical engineering electronic engineering information engineering Bitmap Data Mining 020201 artificial intelligence & image processing Frequent Sequential Patterns Data mining Sequential Pattern Mining computer Algorithm Sequence Mining |
| Zdroj: | ICDM Proceedings of the Sixth International Conference on Data Mining (ICDM'06) International Conference on Data Mining (ICDM'06) International Conference on Data Mining (ICDM'06), 2006, Hong Kong SAR China. pp.6 HAL |
| Popis: | Sequential pattern mining allows to discover temporal relationship between items within a database. The patterns can then be used to generate association rules. When the databases are very large, the execution speed and the memory usage of the mining algorithm become critical parameters. Previous research has focused on either one of the two parameters. In this paper, we present bitSPADE, a novel algorithm that combines the best features of SPAM, one of the fastest algorithm, and SPADE, one of the most memory efficient algorithm. Moreover, we introduce a new pruning strategy that enables bitSPADE to reach high performances. Experimental evaluations showed that bitSPADE ensures an efficient tradeoff between speed and memory usage by outperforming SPADE by both speed and memory usage factors more than 3.4 and SPAM by a memory consumption factor up to more than an order of magnitude. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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