A Novel Initialization Method for Particle Swarm Optimization-based FCM in Big Biomedical Data
Department of Quantitative Health Sciences
Bioinformatics | Numerical Analysis and Scientific Computing | Theory and Algorithms
Based on empirical studies, the feature of random initialization in Particle Swarm Optimization (PSO) based Fuzzy c-means (FCM) methods affects the computational performance especially in big data. As the data points in high-density areas are more likely near the cluster centroids, we design a new algorithm to guide the initialization according to the data density patterns. Our algorithm is initialized by fusing the data characteristics near the cluster centers. Our evaluation results from real data show that our approach can significantly improve the computational performance of PSO-based Fuzzy clustering methods, while preserving comparable clustering performance.
Initialization, FCM, Patterns, Particle Swarm Optimization, Big Data
DOI of Published Version
Wang CJ,Fang H, Wang C, Daneshmand M, Wang H. A Novel Initialization Method for Particle Swarm Optimization-based FCM in Big Biomedical Data. IEEE BigData 2015. 2942 – 2944, DOI: 10.1109/BigData.2015.7364130.
IEEE International Conference on Big Data (Big Data)
Wang, Chanpaul Jin; Fang, Hua (Julia); Wang, Chonggang; Daneshmand, Mahmoud; and Wang, Honggang, "A Novel Initialization Method for Particle Swarm Optimization-based FCM in Big Biomedical Data" (2015). Quantitative Health Sciences Publications and Presentations. 1157.