Abstract: Mining high utility itemset from XML database refers to the discovery of itemsets with high utility like profits. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing large number of candidate itemset for high utility itemsets. Such a large number of candidate itemset degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains large number of long transactions or long high utility itemsets (HUIs).The algorithm used here is UP-Growth (Utility Pattern Growth) for mining high utility itemsets with a set of techniques for pruning candidate itemsets. The information of high utility itemsets is maintained in a special data structure named UP-Tree (Utility Pattern Tree) such that the candidate itemsets can be generated efficiently with only two scans of the database. UP-Growth not only reduces the number of candidates effectively but also out performs other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions. We can efficiently store and retrieve the data’s in and from the XML databases than relational database.
Keywords: Itemset Mining, UP-Growth, LP-Tree.