quality {evaluomeR} | R Documentation |
The goodness of the classifications are assessed by validating the clusters generated. For this purpose, we use the Silhouette width as validity index. This index computes and compares the quality of the clustering outputs found by the different metrics, thus enabling to measure the goodness of the classification for both instances and metrics. More precisely, this goodness measurement provides an assessment of how similar an instance is to other instances from the same cluster and dissimilar to all the other clusters. The average on all the instances quantifies how appropriately the instances are clustered. Kaufman and Rousseeuw suggested the interpretation of the global Silhouette width score as the effectiveness of the clustering structure. The values are in the range [0,1], having the following meaning:
There is no substantial clustering structure: [-1, 0.25].
The clustering structure is weak and could be artificial: ]0.25, 0.50].
There is a reasonable clustering structure: ]0.50, 0.70].
A strong clustering structure has been found: ]0.70, 1].
quality(data, k = 5, getImages = TRUE)
data |
A |
k |
Positive integer. Number of clusters between [2,15] range. |
getImages |
Boolean. If true, a plot is displayed. |
A SummarizedExperiment
containing the silhouette width measurements and
cluster sizes for cluster k
.
Kaufman L, Rousseeuw PJ (2009). Finding groups in data: an introduction to cluster analysis, volume 344. John Wiley & Sons.
# Using example data from our package data("ontMetrics") result = quality(ontMetrics, k=4)