| Combining multiple clusterings using similarity graph S Mimaroglu, E Erdil Pattern Recognition 44 (3), 694-703, 2011 | 109 | 2011 |
| DICLENS: Divisive clustering ensemble with automatic cluster number S Mimaroglu, E Aksehirli IEEE/ACM transactions on computational biology and bioinformatics 9 (2), 408-420, 2011 | 58 | 2011 |
| CLICOM: Cliques for combining multiple clusterings S Mimaroglu, M Yagci Expert Systems With Applications 39 (2), 1889-1901, 2012 | 29 | 2012 |
| Improving DBSCAN’s execution time by using a pruning technique on bit vectors S Mimaroglu, E Aksehirli Pattern Recognition Letters 32 (13), 1572-1580, 2011 | 23 | 2011 |
| An efficient and scalable family of algorithms for combining clusterings S Mimaroglu, E Erdil Engineering Applications of Artificial Intelligence 26 (10), 2525-2539, 2013 | 22 | 2013 |
| Obtaining better quality final clustering by merging a collection of clusterings S Mimaroglu, E Erdil Bioinformatics 26 (20), 2645-2646, 2010 | 18 | 2010 |
| Approximate Computation of Object Distances by Locality-Sensitive Hashing. S Mimaroglu, DA Simovici DMIN, 714-718, 2008 | 16 | 2008 |
| A binary method for fast computation of inter and intra cluster similarities for combining multiple clusterings S Mimaroglu, AM Yagci Proceedings of the 2nd International Conference on Interaction Sciences …, 2009 | 14 | 2009 |
| ASOD: Arbitrary shape object detection S Mimaroglu, E Erdil Engineering Applications of Artificial Intelligence 24 (7), 1295-1299, 2011 | 12 | 2011 |
| Bit sequences and biclustering of text documents S Mimaroglu, K Uehara Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007 …, 2007 | 8 | 2007 |
| Clustering and Approximate Identification of Frequent Item Sets. S Mimaroglu, DA Simovici FLAIRS, 502-506, 2007 | 6 | 2007 |
| Approximative distance computation by random hashing S Mimaroglu, M Yagci, DA Simovici The Journal of Supercomputing 61, 572-589, 2012 | 2 | 2012 |
| NON-INTRUSIVE LOAD MONITORING USING ENSEMBLE MACHINE LEARNING TECHNIQUES S Mimaroglu US Patent 11,544,632, 2023 | 1 | 2023 |
| Mining Approximate Descriptions Using Rough Sets and Genetic Algorithms. S Mimaroglu Journal of Multiple-Valued Logic & Soft Computing 20, 2013 | 1 | 2013 |
| Mining frequent item sets efficiently by using compression techniques S Mimaroglu, C Cubukcu, E Aksehirli, E Erdil Proceedings of the International Conference on Data Science (ICDATA), 1, 2011 | 1 | 2011 |
| Mining Approximative Descriptions of Sets Using Rough Sets DA Simovici, S Mimaroglu 2009 39th International Symposium on Multiple-Valued Logic, 66-71, 2009 | 1 | 2009 |
| Non-intrusive load monitoring using machine learning and processed training data S Mimaroglu, A Gunel, O Benjamin, S Anqi US Patent 11,989,668, 2024 | | 2024 |
| Operating data anomaly detection and remediation SN Mimaroglu, S Anqi, A Chauhan US Patent App. 18/069,534, 2024 | | 2024 |
| Trained Models for Discovering Target Device Presence S Mimaroglu, O Benjamin, A Gunel, S Anqi, F Ziran US Patent App. 18/391,298, 2024 | | 2024 |
| Trained models for discovering target device presence S Mimaroglu, O Benjamin, A Gunel, S Anqi, F Ziran US Patent 11,893,487, 2024 | | 2024 |
Ertunc ErdilPostdoctoral Researcher, Computer Vision Laboratory, ETH ZurichDirección de correo verificada de vision.ee.ethz.ch
