Finding recent frequent itemsets adaptively over online data streams

By Joong Hyuk Chang, W. Lee. Knowledge Discovery and Data Mining, 2003

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Online Machine Learning in Big Data Streams (2018, background): “st a prescribed frequency. Several variants of the task are described in [8]. Online frequent itemset mining algorithms are surveyed in [57]. Algorithms based on counts of all past data in the stream [48, 101, 155, 234, 152] are also called landmark window based approaches. In some of these algorith”
Foundations and Advances in Data Mining (2005, methodology): “We have been applying LRA using nonparametric regression analysis to discover rules from functional brain images[30],[34],[9], [36],[37].”
A Survey of Sequential Pattern Mining (2017, methodology): “Numerous extensions of the problem of itemset mining are similar to variations of the sequential pattern mining problem such as high-utility itemset mining [71, 36, 126], uncertain itemset mining [121, 49, 50], fuzzy itemset mining [16, 73], and stream itemset mining [12, 102].”
Moment: maintaining closed frequent itemsets over a stream sliding window (2004, background): “Chang et al [5] presented an algorithm,estDec, that mines recent frequent itemsets where the frequency is defined by an aging function.”
Frequent pattern mining: current status and future directions (2007, background): “Chang and Lee (2003) proposed an algorithm for ﬁnding recent frequent itemsets adaptively over an online data stream by decaying the effect of old transactions.”