The full text of this article


A fine-grained scheduling strategy for improving the performance of parallel frequent itemsets mining
by Chao-Chin Wu; Lien-Fu Lai; Liang-Tsung Huang; Syun-Sheng Jhan; Chung Lu
International Journal of Computational Science and Engineering (IJCSE), Vol. 6, No. 4, 2011


Abstract: We propose a scheduling strategy in this paper to address the load imbalance problem of the distributed parallel apriori (DPA) algorithm published recently. We use fine grained tasks that are derived by dividing the tasks defined by DPA into smaller subtasks. The subtasks will be scheduled by a dynamic self-scheduling scheme for better load balance. Furthermore, we propose two different methods for data transmission from the master to workers. The first one broadcasts all the frequent k-itemsets to all work nodes while the second one transmits only the required data to each individual work node. Experimental results demonstrate the proposed two approaches both outperform DPA. The first one is more suitable for small datasets and the second one provides steadier performance improvement no matter which self-scheduling scheme is adopted.

Online publication date: Fri, 25-Nov-2011


is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:


    Username:        Password:         

Forgotten your 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