dBlockmodeling (version 0.1.1)

tmklm: Two-Mode KL-Means Heuristic

Description

This function runs two-mode K-means for an \(RO x CO\) network matrix.

Usage

tmklm(A, RC, CC, TLIMIT)

Arguments

A

An \(RO x CO\) two-mode network matrix.

RC

The number of clusters for row objects (\(1 < RC < RO\)).

CC

The number of clusters for column objects (\(1 < CC < CO\)).

TLIMIT

A desired time limit.

Value

The function returns the following:

• vaf - the variance-accounted-for;

• RP - an \(RO\)-dimensional vector of row cluser assignements;

• RC - an \(RC\)-dimensional vector of column cluser assignements;

• restarts - the number of restarts within the time limit.

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

Examples

# NOT RUN {
# Load the Turning Point Project network (Brusco & Doreian, 2015) data.
data("nyt")

# Run two-mode K-means procedure.
res <- tmklm(nyt,RC = 9,CC = 5,TLIMIT = 1)

# See the results.
res
# }