- I added the ‘cosine’ distance to the following functions: ‘Cluster_Medoids’, ‘Clara_Medoids’, ‘predict_Medoids’, ‘Optimal_Clusters_Medoids’ and ‘distance_matrix’.
- I fixed an error case in the .pdf manual of the package (https://github.com/mlampros/ClusterR/issues/16)

- I added parallelization for the
*exact*method of the*AP_preferenceRange*function which is more computationally intensive as the*bound*method - I modified the
*Optimal_Clusters_KMeans*,*Optimal_Clusters_GMM*and*Optimal_Clusters_Medoids*to accept also a contiguous or non-contiguous vector besides single values as a*max_clusters*parameter. However, the limitation currently is that the user won’t be in place to plot the clusters but only to receive the ouput data ( this can be changed in the future however the plotting function for the contiguous and non-contiguous vectors must be a separate plotting function outside of the existing one). Moreover, the*distortion_fK*criterion can’t be computed in the*Optimal_Clusters_KMeans*function if the*max_clusters*parameter is a contiguous or non-continguous vector ( the*distortion_fK*criterion requires consecutive clusters ). The same applies also to the*Adjusted_Rsquared*criterion which returns incorrect output. For this feature request see the following Github issue.

- I moved the
*OpenImageR*dependency in the DESCRIPTION file from ‘Imports’ to ‘Suggests’, as it appears only in the Vignette file.

- I fixed the
*clang-UBSAN*errors

- I updated the README.md file (I removed unnecessary calls of ClusterR in DESCRIPTION and NAMESPACE files)
- I renamed the
*export_inst_header.cpp*file in the src folder to*export_inst_folder_headers.cpp* - I modified the
*Predict_mini_batch_kmeans()*function to accept an armadillo matrix rather than an Rcpp Numeric matrix. The function appers both in*ClusterRHeader.h*file ( ‘inst’ folder ) and in*export_inst_folder_headers.cpp*file ( ‘src’ folder ) - I added the
*mini_batch_params*parameter to the*Optimal_Clusters_KMeans*function. Now, the optimal number of clusters can be found also based on the min-batch-kmeans algorithm (except for the*variance_explained*criterion) - I changed the license from MIT to GPL-3
- I added the
*affinity propagation algorithm*(www.psi.toronto.edu/index.php?q=affinity%20propagation). Especially, I converted the matlab files*apcluster.m*and*referenceRange.m*. - I modified the minimum version of RcppArmadillo in the DESCRIPTION file to 0.9.1 because the Affinity Propagation algorithm requires the
*.is_symmetric()*function, which was included in version 0.9.1

As of version 1.1.5 the ClusterR functions can take tibble objects as input too.

I modified the ClusterR package to a cpp-header-only package to allow linking of cpp code between Rcpp packages. See the update of the README.md file (16-08-2018) for more information.

I updated the example section of the documentation by replacing the *optimal_init* with the *kmeans++* initializer

- I fixed an Issue related to
*NAs produced by integer overflow*of the*external_validation*function. See, the commented line of the*Clustering_functions.R*file (line 1830).

- I added a
*tryCatch*in*Optimal_Clusters_Medoids()*function to account for the error described in Error in Optimal_Clusters_Medoids function#5 issue

- 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