Merge branch 'main' into pybind11

docs/2_method/3_mip.md

@@ -37,5 +37,5 @@ After solving this integer program, the non-zero diagonal entries of $$A$$ repre
 
 ![Example output from the clustering process, where an entry of 1 indicates that time series $$j$$ belongs to cluster with centroid i.](cluster_matrix_formation4.svg)
 
-Finding global optimality can increase the computation time, depending on the number of time series within the dataset and the DTW distances. Therefore, there is also a built-in option to cluster using k-medoids, described in [k-Medoids Clustering](link to that). The k-medoids method is often quicker as it is an iterative approach, however it is subject to getting stuck in local optima. The results in the next section show the timing and memory performance of both MIP clustering and k-medoids clustering using *DTW-C++* compared to other packages.
+Finding global optimality can increase the computation time, depending on the number of time series within the dataset and the DTW distances. Therefore, there is also a built-in option to cluster using k-medoids. The k-medoids method is often quicker as it is an iterative approach, however it is subject to getting stuck in local optima. The results in the next section show the timing and memory performance of both MIP clustering and k-medoids clustering using *DTW-C++* compared to other packages.
 

joss/paper.bib

@@ -150,3 +150,17 @@ @misc{kumtepeli2023fast
       archivePrefix={arXiv},
       primaryClass={eess.SP}
 }
+
+@article{ROUSSEEUW198753,
+title = {Silhouettes: A graphical aid to the interpretation and validation of cluster analysis},
+journal = {Journal of Computational and Applied Mathematics},
+volume = {20},
+pages = {53-65},
+year = {1987},
+issn = {0377-0427},
+doi = {https://doi.org/10.1016/0377-0427(87)90125-7},
+url = {https://www.sciencedirect.com/science/article/pii/0377042787901257},
+author = {Peter J. Rousseeuw},
+keywords = {Graphical display, cluster analysis, clustering validity, classification},
+abstract = {A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.}
+}