Vertical Mining of Frequent Patterns from Uncertain Data

Laila A. Abd-Elmegid, Mohamed E. El-Sharkawi, Laila M. El-Fangary, Yehia K. Helmy


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.

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