Model-Based Clustering for Image Segmentation and Large Datasets via Sampling |
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Authors: | Ron Wehrens Lutgarde MC Buydens Chris Fraley Adrian E Raftery |
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Institution: | (1) Department of Analytical Chemistry, Radboud University, The Netherlands;(2) Department of Statistics, University of Washington, USA |
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Abstract: | The rapid increase in the size of data sets makes clustering all the more important
to capture and summarize the information, at the same time making clustering more
difficult to accomplish. If model-based clustering is applied directly to a large data set, it
can be too slow for practical application. A simple and common approach is to first cluster
a random sample of moderate size, and then use the clustering model found in this way
to classify the remainder of the objects. We show that, in its simplest form, this method
may lead to unstable results. Our experiments suggest that a stable method with better performance can be obtained with two straightforward modifications to the simple sampling
method: several tentative models are identified from the sample instead of just one, and
several EM steps are used rather than just one E step to classify the full data set. We find
that there are significant gains from increasing the size of the sample up to about 2,000,
but not from further increases. These conclusions are based on the application of several
alternative strategies to the segmentation of three different multispectral images, and to
several simulated data sets. |
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