{"id":634,"date":"2026-03-02T22:28:26","date_gmt":"2026-03-02T22:28:26","guid":{"rendered":"https:\/\/ffritze.de\/?p=634"},"modified":"2026-06-02T10:18:15","modified_gmt":"2026-06-02T10:18:15","slug":"k-means-clustering-entwicklung-mit-scratch","status":"publish","type":"post","link":"https:\/\/ffritze.de\/en\/k-means-clustering-entwicklung-mit-scratch\/","title":{"rendered":"K-means-clustering \u2013 development with Scratch"},"content":{"rendered":"

K-Means Clustering is a popular algorithm in the field of unsupervised learning, which is used to divide data into groups or clusters. This approach makes it possible to identify patterns in data without requiring prior labels. For an introduction to the topic and a fun board game to illustrate the concepts of unsupervised learning, you can find information on this page<\/a>. There you will find materials that help you better understand the basics of K-Means Clustering and apply them in practice.<\/p>\n\n\n\n\n\n\n\n

Step 1: Create prerequisites<\/h2>\n\n\n\n

In the first step of the development of the K-Means clustering program in Scratch, we create the basic prerequisites for the process. First, we choose the number of cluster centers, which is set to 3 in this example. This decision allows us to keep the problem small and still make interesting observations.<\/p>\n\n\n\n

Then we set the data points that are to be clustered randomly on the playing field. Ideally, we choose the random conditions in such a way that three loose groups are already formed. This facilitates later clustering and makes the results more vivid.<\/p>\n\n\n\n

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After the data points are placed, we randomly distribute the cluster centers on the field. To improve visualization, we use three different colors for the cluster centers so that they stand out from each other. These steps lay the foundation for further developments of the K-Mean Clustering Algorithm in Scratch.<\/p>\n<\/div>\n\n\n\n

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