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Reseach Article
Support Vector Clustering Algorithm for Cell Formation in Group Technology
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 46 |
| Year of Publication: 2024 |
| Authors: Prafulla C. Kulkarni |
10.5120/ijca2024924083
|
Prafulla C. Kulkarni . Support Vector Clustering Algorithm for Cell Formation in Group Technology. International Journal of Computer Applications. 186, 46 ( Nov 2024), 14-16. DOI=10.5120/ijca2024924083
Abstract
In Group technology, parts with similar geometry, function, material and process are grouped into part families and the corresponding machines are grouped into machine cells. In cluster analysis, one seeks to find the natural groupings in the data. One searches for patterns in the data set by grouping it into clusters. The goal is to find an optimal grouping for which the data within clusters are similar, but the clusters are dissimilar to each other. Many techniques exist to group the data into clusters. Recently, Support Vector Clustering (SVC) has been employed for cluster analysis. In SVC algorithm, data points are mapped from data space to a high dimensional feature space using a Gaussian kernel. The smallest sphere enclosing in feature space is mapped back to data space where it forms cluster boundary. Thus clusters are formed. The scale parameter of Gaussian kernel and soft margin constant are the two parameters which determine clustering form. A data set of Group technology is considered for investigating performance of the algorithm. Part families are formed by SVC algorithm for the data set within a reasonable time as demonstrated.
References
- C. Chu, and M. Tsai, 1990. “A comparison of three array-based clustering techniques for manufacturing cell formation,” International Journal of Production Research, 28(8), 1417-1433. https://doi.org/10.1080/00207549008942802
- T. Gupta, and H. Seifoddini, 1990. “Production data based similarity coefficient for machine-component grouping decisions in the design of cellular manufacturing system,” International Journal of Production Research, 28(7), 1247-1270.
- T. Gupta, 1991. “Clustering algorithms for design of cellular manufacturing system- an analysis of their performance,” Computers & Industrial Engineering, 20(4), 461-468. https://doi.org/10.1016/0360-8352(91)90018-2
- B. R. Sarkar, 1996.“The resemblance coefficients in group technology: A survey and comparative study on relational metrics,” Computers & Industrial Engineering, 30(11), 103–116. https://doi.org/10.1016/0360-8352(95)00024-0
- C. T. Mosier, J. Yelle, and G. Walker, 1997. “Survey of similarity coefficient based methods as applied to group technology configuration problem,” Omega: the International Journal of Management Science, 25(1), 65-79. https://doi.org/10.1016/S0305-0483(96)00045-X
- M. P. Chandrasekharan and R. Rajagopalan 1986, “An ideal seed non-hierarchical clustering algorithm,” International Journal of Production Research, 24(2), 451-464. https://doi.org/10.1080/00207548608919741
- M. P. Chandrasekharan and R. Rajagopalan, 1987 “ZODIAC-an algorithm for concurrent formation of part families and machine cells,” International Journal of Production Research, 25(6), 835-850. https://doi.org/10.1080/00207548708919880
- G. Srinivasan, and T. T. Narendran, 1991, “GRAPHICS: a non-hierarchical clustering algorithm for group technology problem,” International Journal of Production Search, 29(3), 463–478. https://doi.org/10.1080/00207549108930083
- A. Kusiak, and W. S. Chow, 1987, “Efficient solving of the group technology problem,” Journal of Manufacturing Systems, 6(2), 117-124.
- G. Srinivasan, T. T. Narendran, and B. Mahadevan, 1990 “An assignment model for the part families problem in group technology,” International Journal of Production Research, 28(1), 145-152. https://doi.org/10.1080/00207549008942689
- N. Singh, 1993, “Design of cellular manufacturing systems: an invited review”, European Journal of Operational Research, 69(3), 284-291. https://doi.org/10.1016/0377-2217(93)90016-G
- K. Shanker, and A. K. Agrawal, 1997, “Models and solution methodologies for the generalized grouping problem in cellular manufacturing,” International Journal of Production Research, 35(2), 513-538. https://doi.org/10.1080/002075497195885
- V. Venugopal, 1999, “Soft-computing based approaches to the group technology problem: a state-of-the-art review,” International Journal of Production Research, 37(14),3335-3357. https://doi.org/10.1080/002075499190310
- D. Ben-Arieh, E. Triantaphyllou, 1992, “Quantifying data for group technology with weighted fuzzy features,” International Journal of Production Research, 30(6), 1285-1299 http://dx.doi.org/10.1080/00207549208942957
- C. Chu, and J. Hayya, 1991, “A fuzzy clustering approach to manufacturing cell formation”, International Journal of Production Research, 29(7), 1475-1487. https://doi.org/10.1080/00207549108948024
- A. K. Jain, M. Murthy, and P. J. Flynn, 1999, “Data clustering: A review”, ACM Computing Surveys, 31, 264-323. http://archive.ics.uci.edu/ml/
- A. Ben-Hur, D. Horn, H. T. Siegelmann and V. Vapnik 2001, “ Support Vector Clustering”, Journal of Machine Learning Research, 2(1), 125-137. http://dx.doi.org/10.1162/15324430260185565
- J. Sakethanath, and S. K. Shevade, 2006 “An efficient clustering scheme using support vector method”, Pattern Recognition, 39, 1473-1480. http://dx.doi.org/10.1016/j.patcog.2006.03.012
- P. C. Kulkarni, 2024, “Multi-objective machine cell formation by NSGA-II”, Engineering Today Journal, Vol.3(2), 45-53. https://doi.org/10.5937/engtoday2400007K
- H. Li and Y. Ping, 2015 “Recent advances in Support Vector Clustering: Theory and applications”, International Journal of Pattern Recognition and Artificial Intelligence”, Vol.29, No. 01, 1550002. https://doi.org/10.1142/S0218001415500020
- Xu, H. and Wang, H., 1989, “Part family formation for GT applications based on fuzzy Mathematics”, International Journal of Production Research, 27 (9), 1637-1651. http://dx.doi.org/10.1007/s001700170036
- P. C. Kulkarni 2021, “Solving generalized grouping problems in Cellular Manufacturing Systems by genetic algorithms”, Journal of Science and Technology, Vol. 6, Issue. 5, 82-88. https://doi.org/10.46243/jst.2021.v6.i05.pp82-88
- P.C. Kulkarni 2024, “Support vector clustering algorithm for cell formation in Cellular manufacturing systems”, Engineering Today Journal, Volume 3(3), DOI: 10.5937/engtoday2400012K
- Blake, C., and Merz, C. UCI repository of machine learning databases.(1998) https://doi.org/10.1111/1740-9713.01589
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