Name
Abstract | ||
|---|---|---|
The process of data clustering has mainly focused on optimizing a single objective function, and thus, some information is not used for clustering. Therefore, the aim of this study is to propose a multi-objective complete fuzzy clustering model (MoCFC) that simultaneously optimizes data compactness, separation, and connectedness. The model employs two optimization algorithms; AUGMECON and NSGA-II. Using some fuzzy datasets, the results show that AUGMECON has lower convergence and coverage than NSGA-II, but a higher success index. Moreover, in terms of various cluster validity indices, AUGMECON achieves better performance. However, NSGA-II is the better choice if execution time is critical. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/10798587.2016.1209322 | INTELLIGENT AUTOMATION AND SOFT COMPUTING |
Keywords | Field | DocType |
Fuzzy clustering, Multi-objective optimization, NSGA-II algorithm, epsilon-constraint method, Fuzzy data, Cluster connectedness, Cluster compactness, Cluster separation | Data mining,Fuzzy clustering,Canopy clustering algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 2 | 1079-8587 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Parastou Shahsamandi E. | 1 | 0 | 0.34 |
| Soheil Sadi-Nezhad | 2 | 286 | 18.89 |
| Abbas Saghaei | 3 | 41 | 7.70 |