Abstract
Clustering is widely used in many scientific fields. The contribution of enumerating the value of the silhouette is twofold: firstly it can help choosing a suitable cluster count and secondly it can be used to evaluate the quality of a clustering. Enumerating the silhouette exactly is an extremely time-consuming task, especially in big data applications; it is therefore common to approximate its value. This article presents an efficient shared-memory parallel algorithm for approximating the silhouette, which uses a ball tree. The process of initialising the ball tree and enumerating the silhouette are fully parallelised using the OpenMP API. The results of our experiments show that the proposed parallel algorithm substantially increases the speed of computing the silhouette whilst retaining necessary precision for real-world applications.
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.
