Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Algorithms for clustering data
Algorithms for clustering data
Clustering Algorithms
Cluster validity methods: part I
ACM SIGMOD Record
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Fuzzy cluster validation index based on inter-cluster proximity
Pattern Recognition Letters
An objective approach to cluster validation
Pattern Recognition Letters
Model-based evaluation of clustering validation measures
Pattern Recognition
Performance of data resampling methods for robust class discovery based on clustering
Intelligent Data Analysis
An overview of clustering methods
Intelligent Data Analysis
Cluster Analysis
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
Mapping atmospheric pollutants emissions in European countries
Intelligent Data Analysis
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Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters' compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution.