Abstract:

Mining frequent patterns in large transactional database is a highly researched area in the field of data mining. The different existing frequent pattern discovery algorithms suffer from the same problem. That is, they are all inherently dependent on the amount of main memory available. In this paper, we investigate approaches to mining frequent itemsets when the database or data structure used in the mining are too large to fit in main memory. Experimental results show the advantage of our algorithm. The memory requirement is independent from the number of processed transactions which enable truly scalable data mining.

Keywords: data mining, association rules, memory requirements

(Full Paper 497 KB)

BibTex:

@ARTICLE{P1120638003,

AUTHOR = {M.H.Margahny and A.A.Shakour},

TITLE = {Scalable Algorithm for Mining Association Rules},

JOURNAL ={The International Journal of Artificial Intelligence and Machine Learning},

}

(Full Paper 497 KB)