ClassProp

Computes the agreement proportion between two classification vectors.

 cluster

The utility of this package is in 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.

Wei-Chen Chen

MixSim

Simulating Data to Study Performance of Clustering Algorithms

Volodymyr Melnykov

Ranjan Maitra

Robert Davies

Stephen Moshier

Rouben Rostamian

ClassProp function

<dl> <dt>id1</dt>
<dd>first partitioning vector.</dd> <dt>id2</dt>
<dd>second partitioning vector.</dd></dl>

Arguments

Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.

Author

Classification Proportion — ClassProp

<dl>

 <dt>id1</dt>
<dd>first partitioning vector.</dd>

 <dt>id2</dt>
<dd>second partitioning vector.</dd>

</dl>

ClassProp: Classification Proportion

Description

Usage

Value

Arguments

Author

References

See Also

Examples