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In the second step, we use K-means to classify the data. K-means is an unsupervised clustering algorithm designed to partition unlabelled data into a certain number of distinct groupings. Following is the process of the K-means algorithm: First, the algorithm randomly selects a centroid for each cluster. If we choose a K of 3, there will be three centroids with 3 clusters. Next, The shortest Euclidean distance algorithm is employed to calculate the distances between each data point and the cluster centroids. Each data point is then assigned to the cluster associated with the nearest centroid. Third, the centroids of each cluster are recalculated by taking the average of all data points within that cluster. At last, Steps 2 and 3 are repeated iteratively until the cluster centroids no longer change or change very minimally. |
IEEE/ICACT20240269 Slide.09
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