Mining Frequent Patterns in Data Streams at Multiple Time Granularities

By Chris R. Giannella, Jiawei Han, Xifeng Yan, Philip S. Yu. 2002

609 citations recorded by the source corpus.

How researchers see this paper

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Fast and approximate stream mining of quantiles and frequencies using graphics processors (2005, methodology): “The hash-based approaches for frequency counts [10, 12, 18, 26] use a hash table and each item in the stream owns a respective list of counters the table.”
Online Machine Learning in Big Data Streams (2018, background): “In some of these algorithms, time adaptivity is achieved by placing more importance on recent items [48, 101, 152].”
On evaluating stream learning algorithms (2012, background): “…and Riquelme (2004); Gama and Kosina (2011) for decision rules, Li, Wu, and Hu (2010); Katakis, Tsoumakas, and Vlahavas (2010) in change detection, Giannella, Han, Pei, Yan, and Yu (2003); Chi, Wang, Yu, and Muntz (2006) in frequent pattern mining, etc., maintain a model that continuously evolves…”
A survey on data stream clustering and classification (2015, background): “For example, the tree structure in FP-Stream [29] will become very large over time, and the process of updating and scanning over the tree may degrade its performance.”
Random Forests for Big Data (2015, background): “Moreover, data streams can be processed in observation- or time-based windows or even batches which collect a number of recent observations (see for instance [47]).”
Mining data streams: a review (2005, unknown): “[20] have developed a frequent itemsets mining algorithm over data stream.”