Abstract
Clustering is to divide a set of objects into multiple classes, and each class is made up of similar objects. Traditional centralized clustering algorithms cluster objects stored in a single site, but it cannot satisfy the clustering requirements when objects are distributed. Distributed clustering algorithms can satisfy this need, which extracts a classification mode from distributed objects. This paper classifies and analyzes typical distributed clustering algorithms. Two data sets-Iris and Wine are used to compare several distributed clustering algorithms from two metrics: clustering accuracy and clustering time.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
