文章基本信息

标题：Pair Triplet Association Rule Generation in Streams
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作者：Manisha Thool ; Preeti Voditel
期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2013
卷号：2
期号：4
页码：81-89
出版社：IJCSN publisher
摘要：Many applications involve the generation and analysis of a newkind of data, called stream data, where data flows in and out ofan observation platform or window dynamically. Such datastreams have the unique features such as huge or possiblyinfinite volume, dynamically changing, flowing in or out in afixed order, allowing only one or a small number of scans. Animportant problem in data stream mining is that of findingfrequent items in the stream. This problem finds applicationacross several domains such as financial systems, web trafficmonitoring, internet advertising, retail and e-business. Thisraises new issues that need to be considered when developingassociation rule mining technique for stream data. The Space-Saving algorithm reports both frequent and top-k elements withtight guarantees on errors. We also develop the notion ofassociation rules in streams of elements. The Streaming-Rulesalgorithm is integrated with Space-Saving algorithm to report 1-1 association rules with tight guarantees on errors, usingminimal space, and limited processing per element and we areusing Apriori algorithm for static datasets and generation ofassociation rules and implement Streaming-Rules algorithm forpair, triplet association rules. We compare the top- rules ofstatic datasets with output of stream datasets and findpercentage of error
关键词：Association rule mining; Space-Saving algorithm;Streaming-rules algorithm