Teaching system for the k-means clustering technique
Abstract
Clustering task is one of the most used unsupervised classification techniques in data processing, which has the aim of finding an ideal partition of a data set. One of the algorithms that has been applied the most in real and everyday fields is known as the k-means algorithm. There are several platforms that allow the application of this algorithm, but the use of these platforms is limited, since the user cannot identify the processes that the algorithm follows to reach the final result. Given the use of k-means, in order to show a detailed process and therefore known in which cases it is convenient to use it, it is important to have a tool that teaches step by step each one of the operations that it performs to reach its purpose. This work presents a teaching system, which works as a support tool that allows discovering how the partition of a data set is built in order to reinforce the learning of k-means algorithm.
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References
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