IMIDB: An Algorithm for Indexed Mining of Incremental Databases
| Autor: | Tarek F. Gharib, Abdulfattah S. Mashat, Mostafa G. M. Mostafa, Mohammed M. Fouad |
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| Rok vydání: | 2017 |
| Předmět: |
imidb algorithm
Information retrieval Computer science Science InformationSystems_DATABASEMANAGEMENT QA75.5-76.95 010103 numerical & computational mathematics 02 engineering and technology computer.software_genre 01 natural sciences trie data structure indexing large databases Artificial Intelligence Electronic computers. Computer science incremental mining 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining 0101 mathematics computer Software Information Systems |
| Zdroj: | Journal of Intelligent Systems, Vol 26, Iss 1, Pp 69-85 (2017) |
| ISSN: | 2191-026X 0334-1860 |
| Popis: | Association rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size. |
| Databáze: | OpenAIRE |
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