SOTPNARM : SIZE OF TRANSACTION-BASED ON POSITIVE AND NEGATIVE ASSOCIATION RULE

Cognitive Sciences and Application Machine Learning

Association rules mining has many algorithm and technic to solve problems. Apriori algorithm is used in market data base. Support and Confidence are two values to calculate and must be compared with threshold minsupp and threshold minconf proposed by user. Improved algorithm about association rules tries to optimize the numbers of itemsets , the quality of association rules. One of the improved algorithm is the Size Of Transaction-based Association Rule Mining( SOTARM) . This paper shows how to extract item sets frequents positives and negatives using the size of transaction, named SOTPNARM. The aim of our proposed solution is not only the using of negative item in the transaction data base when the original item is unfrequented but the negation of the rare item can be among of the association rules found. We begin with the introduction that tells what sets are item frequent, itemsets frequents. Then the proposed solution using the size of transaction with example illustration followed the discussion about the results and we terminate with the conclusion and the perspective

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