Name
Abstract | ||
|---|---|---|
For the limited application and shortcoming of FCM (Fuzzy C-Means) clustering algorithm, an improved automatic FCM clustering algorithm is put forward. First, the fuzzy equivalent matrix is achieved by fuzzier the standard uniform data sets; then, the objective function of the improved automatic FCM clustering algorithm is optimized by the amendment of membership function and distance measuring function; The Lagrange multiplier optimization algorithm is calculated to update iteration of membership degree and clustering center. Finally, the automatic clustering is obtained by the degree of cohesion and separation. The traffic flow data of an extra long highway tunnel in Shaanxi is taken as an actual example to apply the improved automatic FCM clustering algorithm. The clustering result shows that the validity of clustering is improved using the improved automatic FCM algorithm. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/DBTA.2010.5659043 | DBTA |
Keywords | Field | DocType |
optimisation,fuzzy set theory,pattern clustering,clustering algorithm,traffic flow data,roads,matrix algebra,fcm,traffic flow,membership function,shaanxi,distance measuring function,automatic fcm clustering,fuzzy equivalent matrix,distance measuing function,highway tunnel,lagrange multiplier optimization,fuzzy c-means clustering,indexes,algorithm design and analysis,objective function,noise,lagrange multiplier,classification algorithms,clustering algorithms,robustness | k-medians clustering,Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Artificial intelligence,FLAME clustering,Cluster analysis,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-6977-2 | 2 | 0.39 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuhua Yu | 1 | 2 | 0.39 |
| Hongke Xu | 2 | 10 | 2.66 |
| Limin Wang | 3 | 2 | 0.39 |
| Xiaojian Zhou | 4 | 74 | 9.19 |