An Efficient Method for Mining Erasable Itemsets Using Multicore Processor Platform

Bao Huynh and Bay Vo

Complexity, 2018, vol. 2018, 1-9

Abstract:

Mining erasable itemset (EI) is an attracting field in frequent pattern mining, a wide tool used in decision support systems, which was proposed to analyze and resolve economic problem. Many approaches have been proposed recently, but the complexity of the problem is high which leads to time-consuming and requires large system resources. Therefore, this study proposes an effective method for mining EIs based on multicore processors (pMEI) to improve the performance of system in aspect of execution time to achieve the better user experiences. This method also solves some limitations of parallel computing approaches in communication, data transfers, and synchronization. A dynamic mechanism is also used to resolve the load balancing issue among processors. We compared the execution time and memory usage of pMEI to other methods for mining EIs to prove the effectiveness of the proposed algorithm. The experiments show that pMEI is better than MEI in the execution time while the memory usage of both methods is the same.

Date: 2018
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/8503/2018/8487641.pdf (application/pdf)
http://downloads.hindawi.com/journals/8503/2018/8487641.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8487641

DOI: 10.1155/2018/8487641

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().