DataOrDistances: Either a [1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
or a [1:n,1:n] symmetric distance matrix.
minPts: Classic smoothing factor in density estimates [Campello et al., 2015, p.9]
PlotIt: Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
PlotTree: Default: FALSE, If TRUE plots the dendrogram. If minPts is missing, PlotTree is set to TRUE.
...: Further arguments to be set for the clustering algorithm, if not set, default arguments are used.
Details
"Computes the hierarchical cluster tree representing density estimates along with the stability-based flat cluster extraction proposed by Campello et al. (2013). HDBSCAN essentially computes the hierarchy of all DBSCAN* clusterings, and then uses a stability-based extraction method to find optimal cuts in the hierarchy, thus producing a flat solution."[Hahsler et al., 2019]
It is claimed by the inventors that the minPts parameter is noncritical [Campello et al., 2015, p.35]. minPts is reported to be set to 4 on all experiments [Campello et al., 2015, p.35].
Returns
List of - Cls: [1:n] numerical vector defining the clustering; this classification is the main output of the algorithm. Points which cannot be assigned to a cluster will be reported as members of the noise cluster with 0.
Dendrogram: Dendrogram of hierarchical clustering algorithm
Tree: Ultrametric tree of hierarchical clustering algorithm
Object: Object defined by clustering algorithm as the other output of this algorithm
References
[Campello et al., 2015] Campello, R. J., Moulavi, D., Zimek, A., & Sander, J.: Hierarchical density estimates for data clustering, visualization, and outlier detection, ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 10(1), pp. 1-51. 2015.
[Hahsler et al., 2019] Hahsler M, Piekenbrock M, Doran D: dbscan: Fast Density-Based Clustering with R. Journal of Statistical Software, 91(1), pp. 1-30. doi: 10.18637/jss.v091.i01, 2019