- I added the
*DARMA_64BIT_WORD*flag in the Makevars file to allow the package processing big datasets - I modified the
*kmeans_miniBatchKmeans_GMM_Medoids.cpp*file and especially all*Rcpp::List::create()*objects to addrress the clang-ASAN errors.

- I modified the
*Optimal_Clusters_KMeans*function to return a vector with the*distortion_fK*values if criterion is*distortion_fK*(instead of the*WCSSE*values). - I added the ‘Moore-Penrose pseudo-inverse’ for the case of the ‘mahalanobis’ distance calculation.

- I modified the
*OpenMP*clauses of the .cpp files to address the ASAN errors. - I removed the
*threads*parameter from the*KMeans_rcpp*function, to address the ASAN errors ( negligible performance difference between threaded and non-threaded version especially if the*num_init*parameter is less than 10 ). The*threads*parameter was removed also from the*Optimal_Clusters_KMeans*function as it utilizes the*KMeans_rcpp*function to find the optimal clusters for the various methods.

I modified the *kmeans_miniBatchKmeans_GMM_Medoids.cpp* file in the following lines in order to fix the clang-ASAN errors (without loss in performance):

- lines 1156-1160 : I commented the second OpenMp parallel-loop and I replaced the
*k*variable with the*i*variable in the second for-loop [in the*dissim_mat()*function] - lines 1739-1741 : I commented the second OpenMp parallel-loop [in the
*silhouette_matrix()*function] - I replaced (all) the
*silhouette_matrix*(arma::mat) variable names with*Silhouette_matrix*, because the name overlapped with the name of the Rcpp function [in the*silhouette_matrix*function] - I replaced all
*sorted_medoids.n_elem*with the variable*unsigned int sorted_medoids_elem*[in the*silhouette_matrix*function]

I modified the following *functions* in the *clustering_functions.R* file:

*KMeans_rcpp()*: I added an*experimental*note in the details for the*optimal_init*and*quantile_init*initializers.*Optimal_Clusters_KMeans()*: I added an*experimental*note in the details for the*optimal_init*and*quantile_init*initializers.*MiniBatchKmeans()*: I added an*experimental*note in the details for the*optimal_init*and*quantile_init*initializers.

The *normalized variation of information* was added in the *external_validation* function (https://github.com/mlampros/ClusterR/pull/1)

I fixed the valgrind memory errors

I removed the warnings, which occured during compilation. I corrected the UBSAN memory errors which occured due to a mistake in the *check_medoids()* function of the *utils_rcpp.cpp* file. I also modified the *quantile_init_rcpp()* function of the *utils_rcpp.cpp* file to print a warning if duplicates are present in the initial centroid matrix.

- I updated the dissimilarity functions to accept data with missing values.
- I added an error exception in the predict_GMM() function in case that the determinant is equal to zero. The latter is possible if the data includes highly correlated variables or variables with low variance.
- I replaced all unsigned int’s in the rcpp files with int data types

I modified the RcppArmadillo functions so that ClusterR passes the Windows and OSX OS package check results

I modified the RcppArmadillo functions so that ClusterR passes the Windows and OSX OS package check results