Vertical Mining of Frequent Patterns from Uncertain Data
Vertical Mining of Frequent Patterns from Uncertain Data
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DOI：
10.5539/cis.v3n2p171
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发表时间：
2010-04
期刊：
影响因子：
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通讯作者：
Laila A. Abd El-Megid;M. El-Sharkawi;Laila Mohamed El Fangary;Y. Helmy
中科院分区：
文献类型：
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作者：
Laila A. Abd El-Megid;M. El-Sharkawi;Laila Mohamed El Fangary;Y. Helmy
人们已经开发出高效的算法来挖掘传统数据中的频繁模式，其中每个事务的内容都是明确已知的。有许多处理真实数据集的应用程序，其中事务的内容是不确定的。从不确定数据中挖掘频繁模式的研究工作有限。这是通过扩展目前提出的用于挖掘精确数据的水平算法以适应不确定性环境来实现的。垂直开采是一种很有前途的方法，实验证明它比水平开采更有效。在本文中，我们扩展了最先进的垂直挖掘算法Eclat，用于从不确定数据中挖掘频繁模式，从而产生了提出的UEclat算法。此外，我们将所提出的UEclat算法与uf增长算法进行了比较。实验结果表明，该算法比uf增长算法至少高出一个数量级。
Efficient algorithms have been developed for mining frequent patterns in traditional data where the content of each transaction is definitely known. There are many applications that deal with real data sets where the contents of the transactions are uncertain. Limited research work has been dedicated for mining frequent patterns from uncertain data. This is done by extending the state of art horizontal algorithms proposed for mining precise data to be suitable with the uncertainty environment. Vertical mining is a promising approach that is experimentally proved to be more efficient than the horizontal mining. In this paper we extend the state-of-art vertical mining algorithm Eclat for mining frequent patterns from uncertain data producing the proposed UEclat algorithm. In addition, we compared the proposed UEclat algorithm with the UF-growth algorithm. Our experimental results show that the proposed algorithm outperforms the UF-growth algorithm by at least one order of magnitude.