Abstract
Frequent itemset mining is a focused theme in data mining research and an important step in the analysis of data arising in a broad range of applications. The traditional exact model for frequent itemset requires that every item occur in each supporting transaction. However, real application data is usually subject to random noise. The reasons for noise are human error and measurement error. These reasons pose new challenges for the efficient discovery of frequent Itemset from the noisy data. Approximate frequent itemset mining is the discovery of itemset that are present not exactly but approximately in transactions.Most known approximate frequent Itemset mining algorithms work by explicitly stating the insertion penalty value and weight threshold. This paper presents a new method for generating insertion penalty value and weight threshold using support count of an item.
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