An indicator for the number of modes in a mixture model using a linear map to simplex structure
M. Weber, W. Rungsarityotin and A. Schliep
In From Data and Information Analysis to Knowledge Engineering, Springer, 103–110, 2006. Proceedings of the GfKl 2005.
The problem of clustering data can be formulated as a graph partitioning problem. In this setting, spectral methods for obtaining optimal solutions have received a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and establish a connection to spectral graph partitioning. We show that in our approach a clustering can be efficiently computed by mapping the eigenvector data onto a simplex. To deal with the prevalent problem of noisy and possibly overlapping data we introduce the Min-chi indicator which helps in confirming the existence of a partition of the data and in selecting the number of clusters with quite favorable performance. Furthermore, if no hard partition exists in the data, the Min-chi can guide in selecting the number of modes in a mixture model. We close with showing results on simulated data generated by a mixture of Gaussians.
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