Quasi-erasable itemset mining
| Autor: | Shyue-Liang Wang, Lu-Hung Chen, Tzung-Pei Hong, Chun-Wei Lin, Bay Vo |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry InformationSystems_DATABASEMANAGEMENT 02 engineering and technology computer.software_genre 020901 industrial engineering & automation Production planning New product development 0202 electrical engineering electronic engineering information engineering Information gain ratio 020201 artificial intelligence & image processing Algorithm design Data mining business computer |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2017.8258125 |
| Popis: | Erasable-itemset mining used in production planning identifies itemsets (or components) that, if removed, would not affect profits. Formally, an itemset is erasable if its gain ratio is equal to or smaller than a given maximum gain-ratio threshold r. Since new products with different components may be added, the original batch algorithm will waste time in gathering up-to-date erasable itemsets. In this paper, we propose the concept of the e-quasi-erasable itemsets and use it to improve mining performance. The itemsets in both the original database and the new product can then be divided into erasable, e-quasi-erasable, and nonerasable. Thus, there are nine combinations that are then processed in different ways. Experiments are finally made to verify the performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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