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网格下最大频繁项集挖掘算法的实现 被引量:6

Implementation of Maximal Frequent Itemset Data Mining Based on Grid
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摘要 随着网格和数据挖掘技术的发展,提出了网格平台下最大频繁项集数据挖掘算法,采用数据库的垂直表示和基于前缀关系的等价划分,以等价类长度的指数函数作为等价类的权值,减少剪枝对负载的影响,合理划分等价类,在动态负载平衡情况下使处理机异步计算,大大提高算法的执行效率。实验证明设计的算法有较好的可扩展性,其性能明显优于其他相关算法。 As the development of grid and data mining,advance an algorithm of maximal frequent itemset data mining based on grid. With a vertical database layout scheme and a prefix- based equivalence classes, the algorithm is done by a complete inclusive relation between the equivalence classes of gene itemsets, these techniques eliminate the need of synchronization. The experimental results demonstrate the superb efficiency of the approach in comparison with other relative methods.
出处 《计算机技术与发展》 2007年第1期98-100,共3页 Computer Technology and Development
基金 湖南省教育厅重点项目(04A037) 湖南文理学院教研资助项目
关键词 网格 最大 频繁项集 等价类 数据挖掘 grid maximal frequent itemset equivalence class data mining
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参考文献5

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二级参考文献10

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