Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Cluster analysis: a further approach based on density estimation
Computational Statistics & Data Analysis
Clustering Algorithms
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Inference in model-based cluster analysis
Statistics and Computing
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Clustering via nonparametric density estimation
Statistics and Computing
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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Silhouette information evaluates the quality of the partition detected by a clustering technique. Since it is based on a measure of distance between the clustered observations, its standard formulation is not adequate when a density-based clustering technique is used. In this work we propose a suitable modification of the Silhouette information aimed at evaluating the quality of clusters in a density-based framework. It is based on the estimation of the data posterior probabilities of belonging to the clusters and may be used to measure our confidence about data allocation to the clusters as well as to choose the best partition among different ones.