Comparison of dynamic itemset mining algorithms for multiple support thresholds
| Autor: | Nourhan N. Abuzayed, Belgin Ergenc |
|---|---|
| Přispěvatelé: | TR130596, Abuzayed, Nourhan, Ergenç, Belgin, Izmir Institute of Technology. Computer Engineering |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Association rule mining
Association rule learning Single support Computer science Itemset mining Process (computing) 02 engineering and technology computer.software_genre Execution time Tree (data structure) Dynamic problem Dynamic database 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Algorithm computer Dynamic itemset mining Multiple support thresholds |
| Zdroj: | IDEAS |
| Popis: | 21st International Database Engineering and Applications Symposium, IDEAS 2017; Bristol; United Kingdom; 12 July 2017 through 14 July 2017 Mining1 frequent itemsets is an important part of association rule mining process. Handling dynamic aspect of databases and multiple support threshold requirements of items are two important challenges of frequent itemset mining algorithms. Most of the existing dynamic itemset mining algorithms are devised for single support threshold whereas multiple support threshold algorithms are static. This work focuses on dynamic update problem of frequent itemsets under multiple support thresholds and proposes tree-based Dynamic CFP-Growth++ algorithm. Proposed algorithm is compared to our previous dynamic algorithm Dynamic MIS [50] and a recent static algorithm CFP-Growth++ [2] and, findings are; in dynamic database, 1) both of the dynamic algorithms are better than the static algorithm CFP-Growth++, 2) as memory usage performance; Dynamic CFP-Growth++ performs better than Dynamic MIS, 3) as execution time performance; Dynamic MIS is better than Dynamic CFP-Growth++. In short, Dynamic CFP-Growth++ and Dynamic MIS have a trade-off relationship in terms of memory usage and execution time. The Scientific and Technological Research Council of Turkey (TUBITAK) under ARDEB 3501 Project No: 114E779. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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