Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Information Retrieval
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Cluster Validity Indices for Graph Partitioning
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
MUC4 '92 Proceedings of the 4th conference on Message understanding
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
HS-measure: a hybrid clustering validity measure to interpret road traffic data
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
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The clustering validation and clustering interpretation are the two last steps of clustering process. The validation step permits to evaluate the goodness of clustering results using some measures. Valid results are then generally interpreted and used in cluster analysis. The validity measures are classified into three categories: unsupervised measures, supervised measures and relative measures. Several supervised measures have been proposed to perform supervised evaluation such as entropy, purity, F-measure, Jaccard coefficient and Rand statistic. Generally, these measures evaluate results according to class labels. However, they are not always able to distinguish interpretable clusters because most of them depends on the number of labels. This paper proposes a new supervised evaluation measure - called "homogeneity degree"- that permits to merge the steps of validation and interpretation. Our measure is applied to a real traffic data set and is used to interpret some traffic situations. Comparison with other evaluation measures shows the performance of our proposal.