Bayesian Ying-Yang machine, clustering and number of clusters
Pattern Recognition Letters - special issue on pattern recognition in practice V
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Sum-of-squares based cluster validity index and significance analysis
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
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In this paper we consider a problem of an unsupervised clustering of multidimensional numerical data. We propose a new method for determining an optimal number of clusters in a data set which is based on a parametric model of a Rate-Distortion curve. Theproposed method can be used in conjunction with any suitable clustering algorithm. It was tested with artificial and real numerical data sets and the results of experiments demonstrate empirically not only effectiveness of the method but also its ability to cope with "difficult" cases where other known methods failed.