A Distributed Solution for Mixed Big Data Clustering

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MAHMOUDI M.; DANESHPOUR N.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2018 | Volume:16 | Issue:3 Page(s): 169-182

Download Full-Text

Persian Verion

View:

1,599

Download:

Cites:

Information Journal Paper

Title

A Distributed Solution for Mixed Big Data Clustering

Author(s)

MAHMOUDI M. | DANESHPOUR N. | Issue Writer Certificate

Keywords

Data modificationQ1

distributed computingQ1

clusteringQ2

big dataQ1

mixed data typeQ1

Abstract

Due to the high-speed of information generation and the need for data-knowledge conversion, there is an increasing need for data mining algorithms. clustering is one of the data mining techniques, and its development leads to further understanding of the surrounding environments. In this paper, a dynamic and scalable solution for clustering mixed big data with a lack of data is presented. In this solution, the integration of common distance metrics with the concept of the closest neighborhood, as well as a kind of geometric coding are used. There is also a way to recover missing data in the dataset. By utilizing parallelization and distribution techniques, multiple nodes can be scalable and accelerated. The evaluation of this solution is based on speed, precision, and memory usage criteria compared to other ones.

Cites

No record.

References

Cite

APA: Copy

MAHMOUDI, M., & DANESHPOUR, N.. (2018). A Distributed Solution for Mixed Big Data Clustering. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 16(3 ), 169-182. SID. https://sid.ir/paper/228407/en

Vancouver: Copy

MAHMOUDI M., DANESHPOUR N.. A Distributed Solution for Mixed Big Data Clustering. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2018;16(3 ):169-182. Available from: https://sid.ir/paper/228407/en

IEEE: Copy

M. MAHMOUDI, and N. DANESHPOUR, “A Distributed Solution for Mixed Big Data Clustering,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 16, no. 3 , pp. 169–182, 2018, [Online]. Available: https://sid.ir/paper/228407/en

Related Journal Papers