Efficient Frequent Itemset Discovery Method in Uncertain Data

Sandhya Rawat, Ajit Kumar Shrivastav, Sanjaydeep Singh Lodhi

Abstract


Frequent itemset mining, the task of finding sets of items that frequently occur together in a dataset, has been at the core of the field of data mining for the past sixteen years. In that time, the size of datasets has grown much faster than has the ability of existing algorithms to handle those datasets. Consequently, improvements are needed. In this thesis, we take the classic algorithm for the problem, A Priori, and improve it quite significantly by introducing what we call a vertical sort. We then use the large dataset, web documents to contrast our performance against several state-of-the-art implementations and demonstrate not only equal efficiency with lower memory usage at all support thresholds, but also the ability to mine support thresholds as yet un-attempted in literature. We also indicate how we believe this work can be extended to achieve yet more impressive results.

 

Keywords- Uncertain Databases, Frequent Itemset Mining, Probabilistic Frequent Itemsets.


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DOI: https://doi.org/10.26483/ijarcs.v2i5.828

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