Frequent Itemset Mining Technique in Data Mining
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 5
Abstract
In computer science and data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation). Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. In this paper, we take the classic Apriori algorithm, 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. We have demonstrated that our implementation produces the same results with the same performance as the best of the state-of-the art implementations. In particular, we have started with the classic algorithm for this problem and introduced a conceptually simple idea, sorting the consequences of which have permitted us to outperform all of the available state-of-the-art implementations.
Authors and Affiliations
Sanjaydeep Singh Lodhi , Premnarayan Arya , Dilip Vishwakarma
Keywords
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