omkm {dBlockmodeling}

One-Mode K-Means Heuristic

Description

This functions runs one-mode K-means for an RO x RO network matrix.

Usage

omkm(A, RC, TLIMIT, IDIAG = 0)

omkmNrep(A, RC, REP, IDIAG = 0)

Arguments

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 omkm only.

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 omkmNrep only.

Value

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.

Author(s)

Michael Brusco

References

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

Examples

# 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