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Because data warehouse is frequently changing,incre-mental data leads to old knowledge which is mined formerly un-available. In order to maintain the discovered knowledge and pat-terns dynamically,this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is in-troduced 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 ad-justed 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 item-sets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and effi-cient than other two contrastive methods. Furthermore,there is sig-nificant 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.
Because data warehouse is frequently changing, incre-mental data leads to old knowledge which is mined formerly un-available. In order to maintain the discovered knowledge and pat-terns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is in-troduced 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 ad-justed 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 item-sets. The results of experimental study are very encouraging. It is obvious from experiment that is, IPARUC is more effective and effi-cient than other two contrastive methods. Furthermore, there is there sig-nificant 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.