Improved Maximal Length Frequent Item Set Mining

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.