Check out RSS, or use RSS reader to subscribe this item
Confirmation
Authentication email has already been sent, please check your email box: and activate it as soon as possible.
You can login to My Profile and manage your email alerts.
Sponsored by the Center for Science and Technology Development of the Ministry of Education
Supervised by Ministry of Education of the People's Republic of China
requent pattern (itemset) mining in transactional databases is one of the most well-studied problems in data mining. One obstacle that limits the practical usage of frequent pattern mining is the extremely large number of patterns generated. Such a large size of the output collection makes it difficult for users to understand and use in practice. Even restricting the output to the border of the frequent itemset collection does not help much in alleviating the problem.
In this paper we address the issue of overwhelmingly large output size by introducing and studying the following problem: mining top-k approximate frequent patterns. The union of the power sets of these k sets should satisfy the following conditions: (1) including itemsets with larger support as many as possible and (2) including itemsets with smaller support as few as possible. An integrated objective function is designed to combine these two objectives. Consequently, we derive the upper bounds on objective function an
Keywords:Data Mining, Association Rules, Frequent Pattern, Optimization