中文摘要：

因为数据仓库经常正在变化，增长数据导致先前是 mined 的旧知识无法获得。以便维持发现知识；模式动态地，这研究介绍为全球经常的 patterns-IPARUC 更新的一个新奇算法。一个快速的聚类方法被介绍第一在 IPARUC 把数据库划分成 n 部分，在数据在一样的部分是类似的的地方。然后，在树上的节点在插入过程由动态地被调整“修剪；放回来“让频率下顺序以便他们能被分享到来临优化。从每本地数据集的最后本地的经常的条款集合 mined 被合成全球经常的条款集合。试验性的学习的结果是很令人鼓舞的。它从 IPARUC 是更有效的实验是明显的；比另外的二个对比的方法有效。而且，到在能帮助我们有效地发现有用知识的网用法采矿的网日志分析器的一个原型有重要应用程序潜力，甚至帮助管理器做决定。

英文摘要：

Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically in inserting process by ＂pruning and laying back＂ to keep the frequency descending order so that they can be shared to approaching optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision.