| k− Means clustering with a new divergence-based distance metric: Convergence and performance analysis S Chakraborty, S Das Pattern Recognition Letters 100, 67-73, 2017 | 74 | 2017 |
| Entropy weighted power k-means clustering S Chakraborty, D Paul, S Das, J Xu International Conference on Artificial Intelligence and Statistics, 691-701, 2020 | 68 | 2020 |
| Simultaneous variable weighting and determining the number of clustersˇXA weighted Gaussian means algorithm S Chakraborty, S Das Statistics & Probability Letters 137, 148-156, 2018 | 38 | 2018 |
| Detecting meaningful clusters from high-dimensional data: A strongly consistent sparse center-based clustering approach S Chakraborty, S Das IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 | 35* | 2020 |
| Hierarchical clustering with optimal transport S Chakraborty, D Paul, S Das Statistics & Probability Letters 163, 108781, 2020 | 34 | 2020 |
| Uniform concentration bounds toward a unified framework for robust clustering D Paul, S Chakraborty, S Das, J Xu Advances in Neural Information Processing Systems 34, 8307-8319, 2021 | 16 | 2021 |
| Automated clustering of high-dimensional data with a feature weighted mean shift algorithm S Chakraborty, D Paul, S Das Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6930-6938, 2021 | 14 | 2021 |
| On the strong consistency of feature‐weighted k‐means clustering in a nearmetric space S Chakraborty, S Das Stat 8 (1), e227, 2019 | 12 | 2019 |
| Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach D Paul, S Chakraborty, S Das, J Xu IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (5), 5862-5871, 2022 | 8* | 2022 |
| On the Uniform Concentration Bounds and Large Sample Properties of Clustering with Bregman Divergences D Paul, S Chakraborty, S Das Stat, e360, 2021 | 7 | 2021 |
| Robust principal component analysis: a median of means approach D Paul, S Chakraborty, S Das arXiv preprint arXiv:2102.03403, 2021 | 6 | 2021 |
| Bregman power k-means for clustering exponential family data A Vellal, S Chakraborty, JQ Xu International Conference on Machine Learning, 22103-22119, 2022 | 5 | 2022 |
| t-Entropy: A New Measure of Uncertainty with Some Applications S Chakraborty, D Paul, S Das arXiv e-prints, arXiv: 2105.00316, 2021 | 5 | 2021 |
| Biconvex Clustering S Chakraborty, J Xu arXiv preprint arXiv:2008.01760, 2020 | 5 | 2020 |
| On uniform concentration bounds for Bi-clustering by using the VapnikˇVChervonenkis theory S Chakraborty, S Das Statistics & Probability Letters 175, 109102, 2021 | 4 | 2021 |
| Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering D Paul, S Chakraborty, D Li, D Dunson arXiv preprint arXiv:2008.07110, 2020 | 4 | 2020 |
| On consistent entropy-regularized k-means clustering with feature weight learning: algorithm and statistical analyses S Chakraborty, D Paul, S Das IEEE Transactions on Cybernetics, 2022 | 3 | 2022 |
| Clustering High-dimensional Data with Ordered Weighted Regularization C Chakraborty, S Paul, S Chakraborty, S Das International Conference on Artificial Intelligence and Statistics, 7176-7189, 2023 | 1 | 2023 |
| Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification S Chakraborty, D Paul, S Das arXiv preprint arXiv:2201.01973, 2022 | | 2022 |
| A New Visual Cryptography Scheme with Perfect Contrast using Galois Fields D Paul, S Chakraborty 2019 3rd International Conference on Recent Developments in Control ˇK, 2019 | | 2019 |
Swagatam DasProfessor, Electronics and Communication Sciences Unit, Indian Statistical Institute, KolkataVerified email at isical.ac.in
