omkm {dBlockmodeling} | R Documentation |
This functions runs one-mode K-means for an RO x RO network matrix.
omkm(A, RC, TLIMIT, IDIAG = 0) omkmNrep(A, RC, REP, IDIAG = 0)
A |
An RO x RO one-mode network matrix. |
RC |
The number of clusters for row objects (1 < RC < RO). |
TLIMIT |
A desired time limit.- for function |
IDIAG |
0 if main diagonal to be ignored, any other value it will be included. Default is 0. |
REP |
The number of repetitions - for function |
The function returns the following:
sse
- the sum of the within-block sum-of-squared deviations from the block means;
vaf
- the variance-accounted-for;
RP
- an RO-dimensional vector of row cluser assignements;
restarts
- the number of restarts within the time limit.
Michael Brusco
Brusco, M. J., Doreian, P., & Steinley, D. (2019). Deterministic blockmodeling of signed and two-mode networks: a tutorial with psychological examples. British Journal of Mathematical and Statistical Psychology.
Baier, D., Gaul, W., & Schader, M. (1997). Two-mode overlapping clustering with applications in simultaneous benefit segmentation and market structuring. In R. Klar & O. Opitz (Eds), Classification and knowledge organization (pp. 557-566), Heidelberg: Springer.
Brusco, M., & Doreian, P. (2015). A real-coded genetic algorithm for two-mode KL-means partitioning with application to homogeneity blockmodeling. Social Networks, 41, 26-35. http://dx.doi.org/10.1016/j.socnet.2014.11.007 Žiberna, A. (2020). K-means-based algorithm for blockmodeling linked networks. Social Networks, 61, 153–169. https://doi.org/10.1016/j.socnet.2019.10.006
# Load the notes borrowing data.. data("notesBorrowing") #Run one-mode K-means procedure. res <- omkm(notesBorrowing,RC = 3, TLIMIT = 1, IDIAG = 0) # See the results. res