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Classification of handwritten digits from the MNIST data set.
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This project uses the unsupervised learning algorithm Kmeans to classify the images, therefore, for learning the classifier it uses only the training data set.
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The program applies the Kmeans clustering algorithm using k clusters, first using random initialization (random values in [0,1]) and then using a single image that found from each label.
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The program determines which of the clusters corresponds to which digit and assigns a digit to a cluster using the most common label in it.
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Estimation of the label using closest centroid.