| Robustness of density-based clustering methods with various neighborhood relations EN Nasibov, G Ulutagay Fuzzy Sets and Systems 160 (24), 3601-3615, 2009 | 77 | 2009 |
| A new unsupervised approach for fuzzy clustering EN Nasibov, G Ulutagay Fuzzy Sets and Systems 158 (19), 2118-2133, 2007 | 76 | 2007 |
| A comparison of fuzzy clustering algorithms for bearing fault diagnosis C Li, M Cerrada, D Cabrera, RV Sanchez, F Pacheco, G Ulutagay, ... Journal of Intelligent & Fuzzy Systems 34 (6), 3565-3580, 2018 | 53 | 2018 |
| Fuzzy and crisp clustering methods based on the neighborhood concept: A comprehensive review G Ulutagay, E Nasibov Journal of Intelligent & Fuzzy Systems 23 (6), 271-281, 2012 | 26 | 2012 |
| Classification of thyroid disease by using data mining models: a comparison of decision tree algorithms E Turanoglu-Bekar, G Ulutagay, S Kantarcı-Savas Oxford Journal of Intelligent Decision and Data Sciences 2, 13-28, 2016 | 20 | 2016 |
| Does foreign ownership affect financial performance? An industrial approach F Ecer, G Ulutagay, E Nasiboglu Middle Eastern Finance and Economics 14, 152-166, 2011 | 16 | 2011 |
| Comparative clustering analysis of bispectral index series of brain activity EN Nasibov, G Ulutagay Expert Systems with Applications 37 (3), 2495-2504, 2010 | 16 | 2010 |
| A new approach to the problem of clustering using the fuzzy joint points method EN Nasibov, G Ulutagay Automatic Control and Computer Sciences 39 (6), 8-17, 2005 | 13 | 2005 |
| On the fuzzy joint points method for fuzzy clustering problem EN Nasibov, G Ulutagay Automatic Control and Computer Sciences 40 (5), 33-44, 2006 | 12 | 2006 |
| Performance evaluation of industrial enterprises via fuzzy inference system approach: a case study G Ulutagay, F Ecer, E Nasibov Soft Computing 19 (2), 449-458, 2015 | 9 | 2015 |
| On fuzzy neighborhood based clustering algorithm with low complexity G Ulutagay, E Nasibov Iranian Journal of Fuzzy Systems 10 (3), 1-20, 2013 | 8 | 2013 |
| FN-DBSCAN: a novel density-based clustering method with fuzzy neighborhood relations G Ulutagay, E Nasibov 8th International Conference on Application of Fuzzy Systems and Soft …, 2008 | 8 | 2008 |
| On the analysis of BIS stage epochs via fuzzy clustering E Nasibov, M Özgören, G Ulutagay, A Öniz, S Kocaaslan Walter de Gruyter 55 (3), 147-153, 2010 | 7 | 2010 |
| Challenges and possible solutions to density based clustering FG Yaşar, G Ulutagay 2016 IEEE 8th International Conference on Intelligent Systems (IS), 492-498, 2016 | 6 | 2016 |
| Some preliminary results on the comparison of FCM, GK, FCMFP and FN-DBSCAN for bearing fault diagnosis C Li, M Cerrada, D Cabrera, RV Sanchez, F Pacheco, G Ulutagay, ... 2017 International Conference on Sensing, Diagnostics, Prognostics, and …, 2017 | 5 | 2017 |
| Mean estimation based on fwa using ranked set sampling with single and multiple rankers B Cetintav, G Ulutagay, S Gurler, N Demirel International Conference on Information Processing and Management of …, 2016 | 4 | 2016 |
| C× K-Nearest Neighbor Classification with Ordered Weighted Averaging Distance G Ulutagay, E Nasibov Novel Applications of Intelligent Systems, 105-122, 2016 | 4 | 2016 |
| Forecasting oil consumption of Turkey using adaptive neuro-fuzzy inference system (ANFIS) O Senvar, G Ulutagay, ET Bekar Intelligent systems and decision making for risk analysis and crisis …, 2013 | 4 | 2013 |
| OWA aggregation based CxK-nearest neighbor classification algorithm G Ulutagay, E Nasibov 2012 6th IEEE International Conference Intelligent Systems, 219-224, 2012 | 4 | 2012 |
| Influence of transitive closure complexity in FJP-based clustering algorithms G Ulutagay, EN Nasibov Turkish Journal of Fuzzy Systems 1 (1), 3-20, 2010 | 4 | 2010 |
Efendi Nasibov (Nasiboglu )Bilgisayar Bilimleri Profesörü, Dokuz Eylül ÜniversitesiVerified email at deu.edu.tr
