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A Novel Initialization Method for Particle Swarm Optimization-based FCM in Big Biomedical Data
Quantitative Health Sciences Publications and Presentations
  • Chanpaul Jin Wang, University of Massachusetts Medical School
  • Hua (Julia) Fang, University of Massachusetts Medical School
  • Chonggang Wang, InterDigital
  • Mahmoud Daneshmand, Stevens Institute of Technology
  • Honggang Wang, University of Massachusetts - Dartmouth
UMMS Affiliation
Department of Quantitative Health Sciences
Publication Date
11-1-2015
Document Type
Article
Abstract

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.

Keywords
  • Initialization,
  • FCM,
  • Patterns,
  • Particle Swarm Optimization,
  • Big Data
DOI of Published Version
10.1109/BigData.2015.7364130
Source
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.
Comments

Paper presented at the 2015 IEEE International Conference on Big Data (Big Data), held Oct. 29, 2015-Nov. 1, 2015, Santa Clara, CA, USA.

Citation Information
Chanpaul Jin Wang, Hua (Julia) Fang, Chonggang Wang, Mahmoud Daneshmand, et al.. "A Novel Initialization Method for Particle Swarm Optimization-based FCM in Big Biomedical Data" (2015)
Available at: http://works.bepress.com/hua_fang/36/