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...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.展开更多
The paper proposes a novel symmetrical encoding-based index structure, which is called EDD-tree （for encoding-based dual distance tree）, to support fast k-nearest neighbor （k-NN） search in high-dimensional spaces....The paper proposes a novel symmetrical encoding-based index structure, which is called EDD-tree （for encoding-based dual distance tree）, to support fast k-nearest neighbor （k-NN） search in high-dimensional spaces. In the EDD-tree, all data points are first grouped into clusters by a k-means clustering algorithm. Then the uniform ID number of each data point is obtained by a dual-distance-driven encoding scheme, in which each cluster sphere is partitioned twice according to the dual distances of start- and centroid-distance. Finally, the uniform ID number and the centroid-distance of each data point are combined to get a uniform index key, the latter is then indexed through a partition-based B^＋-tree. Thus, given a query point, its k-NN search in high-dimensional spaces can be transformed into search in a single dimensional space with the aid of the EDD-tree index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of our proposed scheme, and the results demonstrate that this method outperforms the state-of-the-art high-dimensional search techniques such as the X-tree, VA-file, iDistance and NB-tree, especially when the query radius is not very large.展开更多
基金Supported by the National Natural Science Foundation of China(60472099)Ningbo Natural Science Foundation(2006A610017)
文摘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.
基金the key program of the National Natural Science Foundation of China (Grant No.60533090)the National Natural Science Fund for Distinguished Young Scholars (Grant No.60525108)China-America Academic Digital Library Project
文摘The paper proposes a novel symmetrical encoding-based index structure, which is called EDD-tree （for encoding-based dual distance tree）, to support fast k-nearest neighbor （k-NN） search in high-dimensional spaces. In the EDD-tree, all data points are first grouped into clusters by a k-means clustering algorithm. Then the uniform ID number of each data point is obtained by a dual-distance-driven encoding scheme, in which each cluster sphere is partitioned twice according to the dual distances of start- and centroid-distance. Finally, the uniform ID number and the centroid-distance of each data point are combined to get a uniform index key, the latter is then indexed through a partition-based B^＋-tree. Thus, given a query point, its k-NN search in high-dimensional spaces can be transformed into search in a single dimensional space with the aid of the EDD-tree index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of our proposed scheme, and the results demonstrate that this method outperforms the state-of-the-art high-dimensional search techniques such as the X-tree, VA-file, iDistance and NB-tree, especially when the query radius is not very large.
