Accelerated Density-Based Clustering using Bayesian Sequential Partitioning
Document Type
Conference Proceeding
Publication Date
9-28-2020
Department
Department of Electrical and Computer Engineering
Abstract
This paper presents our work on improving an existing density-based clustering algorithm. By using Bayesian sequential partitioning (BSP) in the density estimation part of the algorithm, we were able to significantly reduce the computational complexity of the clustering algorithm. Simulation results showed 15 to 40% reduction in computation time, depending on the dimensions of the data, while retaining the clustering accuracy of the original algorithm.
Publication Title
2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Recommended Citation
Majdara, A.,
&
Nooshabadi, S.
(2020).
Accelerated Density-Based Clustering using Bayesian Sequential Partitioning.
2020 IEEE International Symposium on Circuits and Systems (ISCAS).
http://doi.org/https://doi.org/10.1109/ISCAS45731.2020.9181237
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14696