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Research Article Open Access
Frequent item set mining is one of the most popular field and most common field of data mining. At the same time, it is a very complex and a time consuming process. Although there are many algorithms are available to mine the frequent patterns from a voluminous data set, but there is still a lot of scope to mine frequent data from different data sets in less time & in less memory. Frequent pattern mining is very useful in cross marketing, market basket analysis, credit card fraud detection. Knowledge discovery in databases (KDD) helps to identifying precious information in such huge databases. This information helps the decision makers in making decision. Ultimately this type of information helps in various goals like – sales increase, profit maximization, prediction etc. In this paper, we have proposed a novel compact data structure based method to discover frequent pattern mining. The proposed method transforms the original data set into a transformed and compacted data set & then it discovers the frequent patterns from the transformed data set.
Data Mining, Association Rule, Support, Confidence, Frequent Item-sets., Memory