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Firstly, determine the number of attributes used, specify the attribute weights for each attribute using the interval of at each attribute with the most important priority, calculate the normalization of each attribute, assign the attribute parameter value to each attribute for each alternative, specify the value of the utility by changing the attribute value in each attribute becomes the value of the raw data attribute and determine the final value of each attribute by diverting the values obtained from the normalization of the raw data attribute value with the normalization value of the attribute weights.
Frequent itemset mining is one of popular data mining technique with frequent pattern or itemset as representation of data.
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