ktaucenters - Robust Clustering Procedures
A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).
Last updated 10 months ago
3.18 score 1 packages 5 scripts 199 downloadsRMBC - Robust Model Based Clustering
A robust clustering algorithm (Model-Based) similar to Expectation Maximization for finite mixture normal distributions is implemented, its main advantage is that the estimator is resistant to outliers, that means that results of parameter estimation are still correct when there are atypical values in the sample (see Gonzalez, Maronna, Yohai and Zamar (2021) <https://arxiv.org/abs/2102.06851>).
Last updated 4 years ago
2.70 score 183 downloads