Weighted Association Rule Mining using weighted support and significance framework

By Feng Tao, Fionn Murtagh, Mohsen M. Farid. Knowledge Discovery and Data Mining, 2003

458 citations recorded by the source corpus.

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Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases (2009, background): “Let I ¼ fi1; i2; . . . . . . img be a set of items and D be a transaction database fT1; T2; . . . . . .”
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases (2013, methodology): “The performance of UP-Growth and UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets.”
Text Classification using Graph Mining-based Feature Extraction (2010, methodology): “[17] proposed a model of weighted support, which satisfies a weighted DC prop erty.”
Isolated items discarding strategy for discovering high utility itemsets (2008, unknown): “rule schemes [29, 33 ], but these algorithms still employ support values of itemsets to”
A fast high utility itemsets mining algorithm (2005, background): “WARM [11] proposes a weighted ARM model where itemset weight is defined as the average weight value of the items comprising this itemset.”
A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets (2005, background): “Researches that assign different weights to items have been proposed in [3, 4, 5, 6].”