Frequent Itemsets Mining in Transactional Databases using Utility Pattern Algorithms

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the non binary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. This paper proposes three novel tree structures to efficiently perform incremental and interactive HUP mining. This propose two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a tree-based data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be generated efficiently with only two scans of database. The first tree structure, Incremental UP Lexicographic Tree (IUPL-Tree), is arranged according to an item’s lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction frequency Tree (IUPTF-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree (IUPTWU-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.