Abstract
Cluster analysis is a process to classify data in a specified data set. In this field, much attention is paid to high-efficiency clustering algorithms. In this paper, the features in the current partition-based and hierarchy-based algorithms are reviewed, and a new hierarchy-based algorithm PHC is proposed by combining advantages of both algorithms, which uses the cohesion and the closeness to amalgamate the clusters. Compared with similar algorithms, the performance of PHC is improved, and the quality of clustering is guaranteed. And both the features were proved by the theoretic and experimental analyses in the paper.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.