Name
Abstract | ||
|---|---|---|
Fuzzy Kohonen clustering networks (FKCN) are well known for clustering analysis (unsupervised learning and self-organizing). This classification of FKCN algorithm is a set of iterative procedures that suffer some major problems, for example its constringency rate is not too fast for a large amount of datasets. To overcome these defects, an efficient fuzzy Kohonen network algorithm is proposed in this paper, which can significantly reduce the computation time required to partition a dataset into desired clusters. By introducing the threshold values and fuzzy convergence operators in the network learning procedure to adjust the learning rates dynamically, the network convergence rate is greatly improved and the error rates of dataset cluster are significantly decreased. Experimental results show the new algorithm is on average three times faster than the original FKCN algorithm. We also demonstrate that the quality of the improved FKCN is better than the original FKCN algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.91 | FSKD (1) |
Keywords | Field | DocType |
efficient fuzzy kohonen clustering,fuzzy set theory,pattern clustering,fuzzy kohonen clustering network learning algorithm,constringency rate,pattern classification,mathematical operators,kcn,fcm,efficient fuzzy kohonen network,clustering analysis,network algorithm,fkcn algorithm,iterative procedure,fuzzy kohonen clustering network,convergence of numerical methods,network convergence rate,improved fkcn,fuzzy classification,original fkcn algorithm,new algorithm,fkcn,unsupervised learning,fuzzy convergence operator,iterative methods,convergence,cluster analysis,error rate,algorithm design and analysis,iris,classification algorithms,clustering algorithms,convergence rate,self organization | Algorithm design,Pattern recognition,Fuzzy classification,Computer science,Fuzzy logic,Fuzzy set,Self-organizing map,Unsupervised learning,Artificial intelligence,Statistical classification,Cluster analysis,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3305-6 | 4 |
PageRank | References | Authors |
0.50 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanqing Yang | 1 | 4 | 0.50 |
| Zhenhong Jia | 2 | 29 | 15.13 |
| Chun Chang | 3 | 4 | 0.50 |
| Xizhong Qin | 4 | 5 | 2.88 |
| Tao Li | 5 | 4 | 0.84 |
| Hao Wang | 6 | 4 | 0.50 |
| Junkai Zhao | 7 | 22 | 2.62 |