Data: [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.
Knn: Number of neighbors to consider to calculate the shared nearest neighbors.
Radius: Eps [Ester et al., 1996, p. 227] neighborhood in the R-ball graph/unit disk graph), size of the epsilon neighborhood. If NULL, automatic estimation is done using insights of [Ultsch, 2005].
minPts: Number of minimum points in the eps region (for core points). In principle minimum number of points in the unit disk, if the unit disk is within the cluster (core) [Ester et al., 1996, p. 228]. if NULL, its 2.5 percent of points.
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
UpperLimitRadius: Limit for radius search, experimental
...: Further arguments to be set for the clustering algorithm, if not set, default arguments are used.
Details
..
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.
Object: Object defined by clustering algorithm as the other output of this algorithm
References
[Ertoz et al., 2003] Levent Ertoz, Michael Steinbach, Vipin Kumar: Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data, SIAM International Conference on Data Mining, 47-59, 2003.