Simulating Data to Study Performance of Clustering Algorithms
Description
MixSim allows simulating mixtures of Gaussian
distributions with different levels of overlap between mixture
components. Pairwise overlap, defined as a sum of two
misclassification probabilities, measures the degree of
interaction between components and can be readily employed to
control the clustering complexity of datasets simulated from
mixtures. These datasets can then be used for systematic
performance investigation of clustering and finite mixture
modeling algorithms. Among other capabilities of MixSim, there
are computing the exact overlap for Gaussian mixtures,
simulating Gaussian and non-Gaussian data, simulating outliers
and noise variables, calculating various measures of agreement
between two partitionings, and constructing parallel
distribution plots for the graphical display of finite mixture
models.