A time- and memory-efficient frequent itemset discovering algorithm for association rule mining Online publication date: Mon, 08-Jan-2007
by Renata Ivancsy, Istvan Vajk
International Journal of Computer Applications in Technology (IJCAT), Vol. 27, No. 4, 2006
Abstract: Frequent itemset discovering is a highly researched area in the field of data mining. The algorithms dealing with this problem have several advantages and disadvantages regarding their time complexity, I/O cost and memory requirement. There are algorithms that have moderate memory usage but high I/O cost, thus the execution time of them is high; such methods are for example the level-wise algorithms. Other methods have advantageous time behaviour; however, they are memory intensive, like the two-phase algorithms. In this paper, a novel algorithm, which is efficient both in time and memory, is proposed. The new algorithm discovers the small frequent itemsets quickly by taking advantage of the easy indexing opportunity of the suggested candidate storage structure. The main benefit of the novel algorithm is its advantageous time behaviour when using different types of datasets as well as its low I/O activity and moderate memory requirement.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook