An Improved Approach for Mining Frequent Itemsets from Uncertain Data using Compact Tree Structure

Information Technology

There are too many existing algorithms proposed that mines frequent patterns from some or precise data. But now a day of requirement for uncertain data mining is increased. There are a lot of actual situations in which data are uncertain, where the mining industry is necessary. For reason of frequent mining from uncertain data mainly two approaches are proposed that are step by step and diagram approach to growth. Level approach rational use to generate and test strategy. U-apriori algorithm is the example of the step-by-step approach. Growth approach-pattern uses tree as the structure of the format. The UF-algorithm growth, PFU-algorithm of growth, algorithm of mines CUFP, PUF-algorithm for growth are the example of the real world of Pattern approach to growth. We take here the study of algorithms that are used at the mine frequent patterns from uncertain data to maintain the data generated from real-world applications.

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