Bibliographic Details
| Title: |
DDCM: a decentralized density clustering and its results gathering approach. |
| Authors: |
Zou, Lida1,2 (AUTHOR) zoulida@sdufe.edu.cn |
| Superior Title: |
Neural Computing & Applications. Dec2023, Vol. 35 Issue 35, p24743-24754. 12p. |
| Subject Terms: |
*DATA mining, *SCALABILITY |
| Abstract: |
The use of distributed clustering is an important method of solving large-scale data mining problems. There are still some problems associated with distributed clustering, such as a performance bottleneck on the master node and network congestion caused by global broadcasting. This paper proposes a decentralized clustering method based on density clustering and the content-addressable network technique. It can form a cluster with excellent scalability and load balancing capabilities based on several surrounding nodes. In addition, a method is presented for optimizing the way clustering results are gathered in different application scenarios. Based on our extensive experiments, the proposed approach performs three times better than benchmark algorithms in terms of efficiency and has a stable expanding ratio of about 0.6 for large-scale data sets. [ABSTRACT FROM AUTHOR] |
|
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Academic Search Premier |
|
Full text is not displayed to guests. |
Login for full access.
|