Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset
نویسندگان
چکیده
منابع مشابه
Frequent Pattern Mining under Multiple Support Thresholds
Traditional methods use a single minimum support threshold to find out the complete set of frequent patterns. However, in real word applications, using single minimum item support threshold is not adequate since it does not reflect the nature of each item. If single minimum support threshold is set too low, a huge amount of patterns are generated including uninteresting patterns. On the other h...
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Mining frequent patterns in transactional databases is an important part of the association rule mining. Frequent pattern mining algorithms with single minsup leads to rare item problem. Instead of setting single minsup for all items, we have used multiple minimum supports to discover frequent patterns. In this research, we have used multiple item support tree (MIS-Tree for short) to mine frequ...
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Many data mining techniques consist in discovering patterns frequently occurring in the source dataset. Typically, the goal is to discover all the patterns whose frequency in the dataset exceeds a userspecified threshold. However, very often users want to restrict the set of patterns to be discovered by adding extra constraints on the structure of patterns. Data mining systems should be able to...
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There are number of existing algorithms proposed that mines frequent patterns from certain or precise data. But know a day’s demand of uncertain data mining is increased. There are many situations in which data are uncertain. For frequent pattern mining from uncertain data mainly two approaches are proposed that are level-wise approach and pattern-growth approach. Level-wise approach use the ge...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2014
ISSN: 0975-8887
DOI: 10.5120/17377-7913