| 摘要: |
| 关联规则的开采是一个重要的数据开采问题.目前已经提出了许多算法用于高效地发现大规模数据库中的关联规则，而对关联规则维护问题的研究工作却很少.在用户开采关联规则的交互过程中，为了找到真正令其感兴趣的规则，用户将需要不断调整两个描述用户兴趣程度的阈值：最小支持度和最小可信度.本文提出了两种增量式更新算法——IUA(incremental updating algorithm)和PIUA(parallel incremental updating algorithm),用来解决这一关联规则高效维护问题. |
| 关键词: 数据开采，知识发现，关联规则，增量式更新，频繁项目集. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家863高科技项目基金资助. |
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| Incremental Updating Algorithms for Mining Association Rules |
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FENG Yu-cai,FENG Jian-lin
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| Abstract: |
| Mining association rules is an important data mining problem. There have been many algorithms proposed for efficient discovery of association rules in large databases. However, very little work has been done on maintenance of discovered association rules. When users interactively mine association rules, they may have to continually tune two thresholds, minimum support and minimum confidence, which describe the users' special interestingness. In this paper, two incremental updating algorithms——IUA and PIUA are presented for such efficient maintenance problem of association rules. |
| Key words: Data mining, knowledge discovery, association rules, incremental updating, frequent itemsets. |
