clusteval 0.1
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NEW FEATURES
* First version of the `clusteval` package. With this package, we aim to provide
tools to evaluate the quality of clusterings and individual clusters obtained
by applying a clustering algorithm to a data set.
CLUSTER EVALUATION METHODS
* `clustomit()` is an implementation of the ClustOmit statistic, which assesses
the cluster omission admissibility condition from Fisher and Van Ness (1971)
to evaluate the stability of a clustering algorithm applied to a data set.
The sampling distribution of the ClustOmit statistic is approximated with a
stratifed, nonparametric bootstrapping scheme, which we compute with the
`mclapply()` function in the `parallel` package.
CLUSTER SIMILARITY
* `cluster_similarity()` computes the similarity between the cluster labels
determined by two clustering algorithms applied to the same data set.
Currently, we have implemented the Jaccard coefficient and the Rand index, each
of which result in proportions with values near 1 suggesting similar
clusterings, while values near 0 suggest dissimilar clusterings.
* `comembership()` calculates the comemberships of all pairs of a vector of
clustering labels. Two observations are said to be comembers if they are
clustered together. We use the `Rcpp` package to calculate quickly the
comemberships for all observations pairs.
* `comembership_table()` calculates the comemberships of all pairs of a vector of
clustering labels obtained from two clustering algorithms and summarizes the
agreements and disagreements between the two clustering algorithms in a 2x2
contingency table. Similar to `comembership()`, we use the `Rcpp` package here
to calculate quickly the comemberships for all observations pairs.
SIMULATED DATA SETS
* `sim_data()` is a wrapper function that generates data from the three
data-generating models given below. By default, each of the models samples
random variates from five populations. The separation between the models and
the origin is controlled by a nonnegative scalar 'delta', which is useful in
determining the efficacy of a clustering algorithm as the population separation
is increased.
* `sim_unif()` generates random variates from five multivariate uniform
populations. The populations do not overlap for values of `delta` greater than
or equal to 1.
* `sim_normal()` generates random variates from multivariate normal populations
with intraclass covariance matrices.
* `sim_student()` generates random variates from multivariate Student's t
populations having a common covariance matrix.
MISCELLANEOUS
* `random_clustering()` randomly clusters a data set into K clusters and is
useful for a baseline comparison of a clustering algorithm.