how do i clasiify non linearly separable data using unsupervised classification methods like k-means?
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for example, iris data set, in which class 2 and 3 are not linearly separable as to class 1 which is linearly separable. Hence this often results in class 2 and 3 getting misclassified as 3 and 2 respectively.
what can be done in such a situation of non linearity between classes for iris and other similar data sets?
i am using k-means function and I am providing it with optimal cluster center's which i generated using global optimization technique.
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Shashank Prasanna
2013-8-26
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KMEANS function in the Statistics Toolbox returns the 4 centeroids. You can compute the distance between an new point and each of the centrioids. The smallest distance will suggest that your data will belong to that cluster.
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