Abstract
This paper presents an improved fuzzy C-means algorithm to solve the manufacturing cell formation problems. The proposed algorithm, which integrates the subtractive algorithm (to produce an initial solution), the fuzzy C-means (FCM) algorithm and a solution selecting procedure (to identify the best solution), remedies the major weaknesses of original FCM clustering. We test the performance of the proposed algorithm with 20 data sets from open literature and 60 generated data sets. Our experiments show that the proposed approach performs much better than the original FCM and the solutions are consistent with the best solutions found in references or the control solutions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1505-1510 |
| Number of pages | 6 |
| Journal | IEEE International Conference on Fuzzy Systems |
| Volume | 2 |
| State | Published - Dec 31 2002 |
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All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics
Cite this
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An improved fuzzy C-means algorithm for manufacturing cell formation. / Li, Jie; Chu, Chao Hsien; Wang, Yunfeng; Yan, Weili.
In: IEEE International Conference on Fuzzy Systems, Vol. 2, 31.12.2002, p. 1505-1510.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - An improved fuzzy C-means algorithm for manufacturing cell formation
AU - Li, Jie
AU - Chu, Chao Hsien
AU - Wang, Yunfeng
AU - Yan, Weili
PY - 2002/12/31
Y1 - 2002/12/31
N2 - This paper presents an improved fuzzy C-means algorithm to solve the manufacturing cell formation problems. The proposed algorithm, which integrates the subtractive algorithm (to produce an initial solution), the fuzzy C-means (FCM) algorithm and a solution selecting procedure (to identify the best solution), remedies the major weaknesses of original FCM clustering. We test the performance of the proposed algorithm with 20 data sets from open literature and 60 generated data sets. Our experiments show that the proposed approach performs much better than the original FCM and the solutions are consistent with the best solutions found in references or the control solutions.
AB - This paper presents an improved fuzzy C-means algorithm to solve the manufacturing cell formation problems. The proposed algorithm, which integrates the subtractive algorithm (to produce an initial solution), the fuzzy C-means (FCM) algorithm and a solution selecting procedure (to identify the best solution), remedies the major weaknesses of original FCM clustering. We test the performance of the proposed algorithm with 20 data sets from open literature and 60 generated data sets. Our experiments show that the proposed approach performs much better than the original FCM and the solutions are consistent with the best solutions found in references or the control solutions.
UR - http://www.scopus.com/inward/record.url?scp=0036458582&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036458582&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0036458582
VL - 2
SP - 1505
EP - 1510
JO - IEEE International Conference on Fuzzy Systems
JF - IEEE International Conference on Fuzzy Systems
SN - 1098-7584
ER -