Name
Abstract | ||
|---|---|---|
Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory. More specifically, we introduce a variant of the Fuzzy C-Means (FCM) clustering algorithm that copes with uncertainty and a similarity measure between intuitionistic fuzzy sets, which is appropriately integrated in the clustering algorithm. We describe an intuitionistic fuzzification of colour digital images upon which we applied the proposed scheme. The experimental evaluation of the proposed scheme shows that it can be more efficient and more effective than the well-established FCM algorithm, opening perspectives for various applications. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1504/IJBIDM.2008.017975 | IJBIDM |
Keywords | Field | DocType |
well-established fcm algorithm,novel fuzzy clustering scheme,fuzzy c-means,intuitionistic fuzzy sets,colour digital image,intuitionistic fuzzy set theory,intuitionistic fuzzy set,real-world clustering problem,intuitionistic fuzzy data,fuzzy clustering,intuitionistic similarity metrics reference: biographical notes:,proposed scheme,clustering algorithm,intuitionistic fuzzification,uncertainty,digital image | Data mining,Fuzzy clustering,Defuzzification,Correlation clustering,Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy set,Artificial intelligence,Fuzzy number,Cluster analysis,Machine learning | Journal |
Volume | Issue | Citations |
3 | 1 | 19 |
PageRank | References | Authors |
0.92 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikos Pelekis | 1 | 881 | 59.28 |
| Dimitris K. Iakovidis | 2 | 234 | 18.81 |
| Evangelos E. Kotsifakos | 3 | 85 | 4.98 |
| Ioannis Kopanakis | 4 | 264 | 16.68 |