Isolated items discarding strategy for discovering high utility itemsets

By Yu-Chiang Li, Jieh-Shan Yeh, Chin-chen Chang. Data & Knowledge Engineering, 2008

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Mining high utility itemsets without candidate generation (2012, methodology): “The three state-of-the-art algorithms had been proven to be superior to other algorithms, such as Two-Phase [18], ShFSM [12], DCG [11], FUM [14], and DCG+ [14].”
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.”
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases (2013, background): “Index Terms—Candidate pruning, frequent itemset, high utility itemset, utility mining, data mining Ç”
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases (2009, background): “Maintaining downward closure property in high utility pattern mining is more challenging as it considers nonbinary frequency values of an item in transactions.”
FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning (2014, methodology): “Many studies have been carried to develop efficient HUIM algorithms [2, 6–8, 10].”