tmklm: Two-Mode KL-Means Heuristic

In dBlockmodeling: Deterministic Blockmodeling of Signed, One-Mode and Two-Mode Networks

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.

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

Examples

# 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

dBlockmodeling documentation built on Aug. 23, 2023, 5:06 p.m.