FPGA implementation of K-means algorithm for bioinformatics application: An accelerated approach to clustering Microarray data

@article{Hussain2011FPGAIO,
  title={FPGA implementation of K-means algorithm for bioinformatics application: An accelerated approach to clustering Microarray data},
  author={Hanaa M. Hussain and Khaled Benkrid and Huseyin Seker and Ahmet T. Erdogan},
  journal={2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)},
  year={2011},
  pages={248-255}
}
The Microarray is a technique used by biologists to perform many genome experiments simultaneously, which produces very large datasets. Analysis of these datasets is a challenge for scientists especially as the number of genome databases is increasing rapidly every year. K-means clustering is an unsupervised data mining technique used widely by bioinformaticians to analyze Microarray data. However, K-means can take between a few seconds to several days to process Microarray data depending on… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 21 REFERENCES

An FPGA Implementation of the K-means Algorithm for Image Processing

M. D. Estlick
  • M.S. thesis,
  • 2002
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

K-means Clustering for Multispectral Images Using Floating-Point Divide

  • 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007)
  • 2007
VIEW 1 EXCERPT

High Speed Document Clustering in Reconfigurable Hardware

  • 2006 International Conference on Field Programmable Logic and Applications
  • 2006
VIEW 1 EXCERPT