文章基本信息

标题：Improved Maximal Length Frequent Item Set Mining
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作者：P. C. S. Nagendra setty ; D. Haritha ; Vedula Venkateswara Rao 等
期刊名称：International Journal of Electronics and Computer Science Engineering
电子版ISSN：2277-1956
出版年度：2012
卷号：1
期号：4
页码：2250-2260
出版社：Buldanshahr : IJECSE
摘要：Association rule mining is one of the most important technique in data mining. Which wide range of applications It aims it searching for intersecting relationships among items in large data sets and discovers association rules. The important of association rule mining is increasing with the demand of finding frequent patterns from large data sources. The exploitation of frequent item set has been restricted by the large number of generated frequent item set and high computational cost in real world applications. To avoid these problems we can use maximum length frequent item sets in generating association rules. The maximum length frequent item sets can be efficiently discovered on very large data sets. At present in research we have LFIMiner algorithm and MaxLFI algorithm to generate maximum length frequent item sets. Here we are proposing a new algorithm called FPMAX for generating maximum length frequent item sets that uses lattice graph data structure
关键词：Data mining; Association rule mining; Maximal length frequent item sets; LFIMiner; MaxLFI Algorithm; FPMAX