Name
Abstract | ||
|---|---|---|
Recently multi-objective clustering has been extensively explored due to its appearance of new applications in many domains. However, in many applications, there is more than a single objective which is needed to be optimized in the context of the application, such as facility location, ad hoc networks and sensor networks. These domains must optimize two objectives of compactness and equi-partitioning which may be conflicted in some situations. Existing algorithms have high complexity. In this paper, we propose an Ensemble Game Theoretic approach for multi-objective clustering method which optimizes two objectives of compactness and equi-partitioning, simultaneously. We compare our algorithm on variety of data sets including synthetic and real ones. The remarkable results are very promising and demonstrate the efficiency of presented approach both in performance and complexity. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3233/AIC-140653 | AI COMMUNICATIONS |
Keywords | Field | DocType |
Multi-objective clustering,game theory,ensemble clustering,equi-partitioning,compactness | Data set,Computer science,Multi-objective optimization,Compact space,Facility location problem,Game theoretic,Artificial intelligence,Wireless ad hoc network,Cluster analysis,Wireless sensor network,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 3 | 0921-7126 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahsa Badami | 1 | 10 | 2.72 |
| Niloofar Mozafari | 2 | 17 | 4.05 |
| Ali Hamzeh | 3 | 214 | 29.47 |
| Sattar Hashemi | 4 | 369 | 34.95 |