Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

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Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases (2013, background): “Then a function Insert_Reorganized_Transaction is called to apply DGN during constructing a global UP-Tree.”
UP-Growth: an efficient algorithm for high utility itemset mining (2010, background): “Existing studies [2, 4, 6, 7, 9] applied overestimated methods to facilitate the mining performance of utility mining.”
Mining high utility itemsets without candidate generation (2012, methodology): “1 Experimental Setup Besides HUI-Miner, our experiments include the following algorithms: IHUPTWU (the fastest one among the algorithms proposed in [5]), UP-Growth [23], and UPGrowth+ [22].”
USpan: an efficient algorithm for mining high utility sequential patterns (2012, background): “Compared to IHUP, UP-Growth is more e.cient, since it further reduces the number of promising patterns which cannot be pruned in IHUP.”
FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning (2014, methodology): “HUIM has a wide range of applications such as website click stream analysis, cross-marketing in retail stores and biomedical applications [2, 7, 10].”