Abstract
This paper presents a novel low-cost and high-performance VLSI architecture for fuzzy c-means clustering. In the architecture, the operations at both the centroid and data levels are pipelined to attain high computational speed while consuming low hardware resources. In addition, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. Experimental results show that the proposed solution is an effective alternative for cluster analysis with low computational cost and high performance.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010 |
| Pages | 112-118 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2010 Dec 1 |
| Event | 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010 - Hong Kong, China Duration: 2010 Dec 11 → 2010 Dec 13 |
Publication series
| Name | Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010 |
|---|
Other
| Other | 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 10/12/11 → 10/12/13 |
Fingerprint
Keywords
- Data clustering
- FPGA
- Fuzzy c-means
- Fuzzy system
- Reconfigurable computing
- System on programmable chip
ASJC Scopus subject areas
- Computer Science (miscellaneous)
Cite this
Fast fuzzy c-means clustering based on low-cost high-performance VLSI architecture in reconfigurable hardware. / Yeh, Yao Jung; Li, Hui Ya; Yang, Cheng Yen; Hwang, Wen Jyi.
Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010. 2010. p. 112-118 5692464 (Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fast fuzzy c-means clustering based on low-cost high-performance VLSI architecture in reconfigurable hardware
AU - Yeh, Yao Jung
AU - Li, Hui Ya
AU - Yang, Cheng Yen
AU - Hwang, Wen Jyi
PY - 2010/12/1
Y1 - 2010/12/1
N2 - This paper presents a novel low-cost and high-performance VLSI architecture for fuzzy c-means clustering. In the architecture, the operations at both the centroid and data levels are pipelined to attain high computational speed while consuming low hardware resources. In addition, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. Experimental results show that the proposed solution is an effective alternative for cluster analysis with low computational cost and high performance.
AB - This paper presents a novel low-cost and high-performance VLSI architecture for fuzzy c-means clustering. In the architecture, the operations at both the centroid and data levels are pipelined to attain high computational speed while consuming low hardware resources. In addition, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. Experimental results show that the proposed solution is an effective alternative for cluster analysis with low computational cost and high performance.
KW - Data clustering
KW - FPGA
KW - Fuzzy c-means
KW - Fuzzy system
KW - Reconfigurable computing
KW - System on programmable chip
UR - http://www.scopus.com/inward/record.url?scp=79951611089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951611089&partnerID=8YFLogxK
U2 - 10.1109/CSE.2010.22
DO - 10.1109/CSE.2010.22
M3 - Conference contribution
AN - SCOPUS:79951611089
SN - 9780769543239
T3 - Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010
SP - 112
EP - 118
BT - Proceedings - 2010 13th IEEE International Conference on Computational Science and Engineering, CSE 2010
ER -