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Cluster sampling examples. Example 1: Assume that a study with relatively large square fixed area plots of 50 m x 50 m had been carried out, on which all tree positions were mapped. Because the individual plots were relatively large, there were only resources available to measure \(n=10\) sample plots.
Next: Hierarchical Classifiers Up: classification Previous: Some special cases Unsupervised Classification - Clustering. A clustering algorithm groups the given samples, each represented as a vector in the N-dimensional feature space, into a set of clusters according to their spatial distribution in the N-D space. Clustering is an unsupervised classification as no a priori knowledge (such as samples of known classes) is assumed to be available. The K-Means Algorithm. Step 1. Randomly choose initial cluster centers from the given sample set (e.g., the first samples of the sample set). Set ; Step 2.