Laboratory works.
BSUIR 2019.
Rather than defining groups before looking at the data, clustering allows you to find and analyze the groups that have formed organically.
k-means clustering is a type of unsupervised learning, which is used when you have data without defined categories or groups. The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable k. The algorithm works iteratively to assign each data point to one of k groups based on the features that are provided. Data points are clustered based on feature similarity.
The results of the k-means clustering algorithm are:
- The centroids of the k clusters, which can be used to label new data.
- Labels for the training data.
Max-min clustering proceeds by choosing an observation at random as the first centroid c1, and by setting the set C of centroids to {c1}. During the ith iteration, ci is chosen such that it maximizes the minimum Euclidean distance between ci and observations in C.
Karalina Dubitskaya
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