【摘要】 关联规则可以揭示数据之间隐含的关系 ,并已在许多领域取得了广泛的应用 .目前已经提出了许多有效发现关联规则的算法 ,这些算法都认为每个数据对规则的重要性相同 .但在实际应用中 ,用户更关心近期发生的数据 ,即历史越久远的数据对规则的影响应该越小 ,应当削弱这些数据对规则的影响 ,为此 ,本文提出了垂直加权关联规则的问题 ;另外 ,用户有时可能希望加强或削弱某些项目对规则的影响 ,即所谓的水平加权关联规则 .最后 ,提出了混合加权关联规则的问题 ,并给出了一个解决该问题的算法 MWAL,实验证明了 MWAL 算法的有效性更多还原

【Abstract】 Association rules are useful for determining correlations between items and have many applications. Lots of algorithms have been proposed for finding the association rules in databases. Most of these algorithms treat each item as uniformity. However, in real applications, the earlier an item occurred, the smaller affection to rules. So we should reduce its affection to rules. To slove this problem, this paper proposed the problem of mining vertical weighted association rules. Another case is the user sometimes may want to mine the association rules with more emphasis on some items, i.e., the problem of mining horizontal weighted association rules. To handle both above problems, in this paper, we propose mixed weighted association rules. Furthermore, we present an algorithm MWAL to handle the problem of mining mixed weighted association rules. The experiments show the efficiency of the algorithms for large databases.更多还原