Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
ACM Computing Surveys (CSUR)
Cluster validity methods: part I
ACM SIGMOD Record
Distance Metrics for Instance-Bsed Learning
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Understanding of Internal Clustering Validation Measures
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
A comparison of internal and external cluster validation indexes
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of the number of clusters using multiple clustering validity indices
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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Clustering methods partition datasets into subgroups with some homogeneous properties, with information about the number and particular characteristics of each subgroup unknown a priori. The problem of predicting the number of clusters and quality of each cluster might be overcome by using cluster validation methods. This paper presents such an approach incorporating quantitative methods for comparison between original and synthetic versions of longitudinal health datasets. The use of the methods is demonstrated by using two different clustering algorithms, K-means and Latent Class Analysis, to perform clustering on synthetic data derived from the 45 and Up Study baseline data, from NSW in Australia.