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41 lines
1.4 KiB
R
41 lines
1.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/method_clustering.R
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\name{clusteriness}
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\alias{clusteriness}
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\title{Perform a cluster analysis.}
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\usage{
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clusteriness(
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data,
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span = 1e+05,
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weight = 0.7,
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n_clusters = NULL,
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relation = NULL
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)
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}
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\arguments{
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\item{data}{The values that should be scored.}
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\item{span}{The maximum span of values considered to be in one cluster.}
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\item{weight}{The weight that will be given to the next largest cluster in
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relation to the previous one. For example, if \code{weight} is 0.7 (the
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default), the first cluster will weigh 1.0, the second 0.7, the third 0.49
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etc.}
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\item{n_clusters}{Maximum number of clusters that should be taken into
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account. By default, all clusters will be regarded.}
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\item{relation}{Number of items that the cluster size should be based on.
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This should always at least the length of the data. By default, the length
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of the data is used.}
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}
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\value{
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A score between 0.0 and 1.0 summarizing how much the data clusters.
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}
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\description{
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This function will cluster the data using \code{\link[stats:hclust]{stats::hclust()}} and
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\code{\link[stats:cutree]{stats::cutree()}}. Every cluster with at least two members qualifies for
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further analysis. Clusters are then ranked based on their size in relation
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to the total number of values. The return value is a final score between
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0.0 and 1.0. Lower ranking clusters contribute less to this score.
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}
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