DAG-searched and Density-based Initial Centroid Location Method for Fuzzy Clustering of Big Biomedical Data

UMMS Affiliation

Department of Quantitative Health Sciences



Document Type

Conference Proceeding


Bioinformatics | Computer Sciences


Randomly allocating initial centroids may lead to undesired steady states for fuzzy c-means (FCM) clustering. This paper proposes an alternative method to automatically search initial centroid location based on data density. Specifically, this method auto-searches points located in high-density domains as centroids using directed acycline graph (DAG) based algorithm, and then iteratively fnding the optimal patterns. Compared with random initialization method, our method seems to have the potential to improve FCM accuracy for larger data size with seconds' tradeoff in computational time using published datasets.


Citation: Chanpaul Jin Wang, Hua Fang, and Honggang Wang. 2014. DAG-searched and density-based initial centroid location method for fuzzy clustering of big biomedical data. In Proceedings of the 8th International Conference on Bioinspired Information and Communications Technologies (BICT '14). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium, Belgium, 290-293. DOI: 10.4108/icst.bict.2014.257932


initial centroids, fuzzy clustering, density, directed acycline graph