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clusteriness: Let weights decrease exponentially

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Elias Projahn 2021-12-08 11:51:32 +01:00
parent d6ee59af4b
commit 8aafcb7555
1 changed files with 8 additions and 2 deletions
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10
R/clusteriness.R
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@ -5,7 +5,13 @@
# further analysis. Clusters are then ranked based on their size in relation # further analysis. Clusters are then ranked based on their size in relation
# to the number of values. The return value is a final score between zero and # to the number of values. The return value is a final score between zero and
# one. Lower ranking clusters contribute less to this score. # one. Lower ranking clusters contribute less to this score.
clusteriness_priv <- function(data, height = 1000000) { #
# @param data The values that should be scored.
# @param height The maximum span of values considered to be in one cluster.
# @param weight The weight that will be given to the next largest cluster in
# relation to the previous one. For example, if `weight` is 0.7 (the default),
# the first cluster will weigh 1.0, the second 0.7, the third 0.49 etc.
clusteriness_priv <- function(data, height = 1000000, weight = 0.7) {
n <- length(data) n <- length(data)
# Return a score of 0.0 if there is just one or no value at all. # Return a score of 0.0 if there is just one or no value at all.
@ -28,7 +34,7 @@ clusteriness_priv <- function(data, height = 1000000) {
if (cluster_size >= 2) { if (cluster_size >= 2) {
cluster_score <- cluster_size / n cluster_score <- cluster_size / n
score <- score + cluster_score / i score <- score + weight ^ (i - 1) * cluster_score
} }
} }