| Random forests for spatially dependent data A Saha, S Basu, A Datta Journal of the American Statistical Association 118 (541), 665-683, 2023 | 45 | 2023 |
| Statistical field calibration of a low-cost PM2. 5 monitoring network in Baltimore A Datta, A Saha, ML Zamora, C Buehler, L Hao, F Xiong, DR Gentner, ... Atmospheric Environment 242, 117761, 2020 | 43 | 2020 |
| Categorical fuzzy k-modes clustering with automated feature weight learning A Saha, S Das Neurocomputing 166, 422-435, 2015 | 38 | 2015 |
| BRISC: bootstrap for rapid inference on spatial covariances A Saha, A Datta Stat 7 (1), e184, 2018 | 25 | 2018 |
| Geometric divergence based fuzzy clustering with strong resilience to noise features A Saha, S Das Pattern Recognition Letters 79, 60-67, 2016 | 24 | 2016 |
| nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes LM Weber, A Saha, A Datta, KD Hansen, SC Hicks Nature communications 14 (1), 4059, 2023 | 17 | 2023 |
| Stronger convergence results for the center-based fuzzy clustering with convex divergence measure A Saha, S Das IEEE Transactions on Cybernetics 49 (12), 4229-4242, 2018 | 17 | 2018 |
| Clustering of fuzzy data and simultaneous feature selection: A model selection approach A Saha, S Das Fuzzy Sets and Systems 340, 1-37, 2018 | 12 | 2018 |
| Axiomatic generalization of the membership degree weighting function for fuzzy C means clustering: Theoretical development and convergence analysis A Saha, S Das Information Sciences 408, 129-145, 2017 | 12 | 2017 |
| On the unification of possibilistic fuzzy clustering: Axiomatic development and convergence analysis A Saha, S Das Fuzzy Sets and Systems 340, 73-90, 2018 | 9 | 2018 |
| Feature-weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective A Saha, S Das Machine Learning 106, 951-992, 2017 | 8 | 2017 |
| Random forests for dependent data A Saha, S Basu, A Datta arXiv preprint arXiv:2007.15421, 2020 | 7 | 2020 |
| Automated feature weighting in clustering with separable distances and inner product induced norms–A theoretical generalization A Saha, S Das Pattern Recognition Letters 63, 50-58, 2015 | 6 | 2015 |
| Scalable predictions for spatial probit linear mixed models using nearest neighbor Gaussian processes A Saha, A Datta, S Banerjee Journal of data science: JDS 20 (4), 533, 2022 | 4 | 2022 |
| RandomForestsGLS: An R package for Random Forests for dependent data A Saha, S Basu, A Datta Journal of open source software 7 (71), 2022 | 3 | 2022 |
| Optimizing cluster structures with inner product induced norm based dissimilarity measures: Theoretical development and convergence analysis A Saha, S Das Information Sciences 372, 796-814, 2016 | 3 | 2016 |
| Inferring independent sets of Gaussian variables after thresholding correlations A Saha, D Witten, J Bien Journal of the American Statistical Association, 1-20, 2024 | 1 | 2024 |
| Random forests for binary geospatial data A Saha, A Datta arXiv preprint arXiv:2302.13828, 2023 | 1 | 2023 |
| Uncertainty Quantification of Random-Forest-Based Estimates of Global Export Production A Saha, BF Jonsson, G Hooker, D Witten, J Bien 2024 Ocean Sciences Meeting, 0 | | |
Swagatam DasProfessor, Electronics and Communication Sciences Unit, Indian Statistical Institute, KolkataVerified email at isical.ac.in
