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.
LC: Lines and Columns of a very small SOM, usually every unit is a cluster, will be ignored if ClusterNo is not NULL.
ClusterNo: Optional, A number k which defines k different clusters to be built by the algorithm. LC will then be set accordingly.
Mode: Either "batch" or "online"
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
rlen: Please see supersom
alpha: Please see supersom
...: Further arguments to be set for the clustering algorithm in somgrid, if not set, default arguments are used.
Details
This clustering algorithm is based on very small maps and, hence, not emergent (c.f. [Thrun, 2018, p.37]). A 3x3 map means 9 units leading to 9 clusters.
Batch is a deterministic clustering approach whereas online is a stochastic clustering approach and research indicates that online should be preferred (c.f. [Thrun, 2018, p.37]).
Returns
List of - Cls: [1:n] numerical vector defining the classification as the main output of the clustering algorithm
Object: Object defined by clustering algorithm as the other output of this algorithm
References
[Wherens, Buydens, 2017] R. Wehrens and L.M.C. Buydens, J. Stat. Softw. 21 (5), 2007; R. Wehrens and J. Kruisselbrink, submitted, 2017.
[Thrun, 2018] Thrun, M.C., Projection Based Clustering through Self-Organization and Swarm Intelligence. 2018, Heidelberg: Springer.