{"id":36447,"date":"2024-11-01T09:48:34","date_gmt":"2024-11-01T09:48:34","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36447"},"modified":"2024-11-01T11:53:08","modified_gmt":"2024-11-01T11:53:08","slug":"deep-learning-pytorch-course-k-means-clustering","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36447\/","title":{"rendered":"Deep Learning PyTorch Course, K-Means Clustering"},"content":{"rendered":"
\n

The approach to data analysis has significantly changed due to the advancement of deep learning and machine learning. One of these is clustering technology. This post explains how to implement the K-means clustering algorithm in PyTorch and utilize it for data analysis.<\/p>\n

1. What is K-means Clustering?<\/h2>\n

K-means clustering is one of the non-supervised learning algorithms that divides the given data points into K clusters. The goal of this algorithm is to minimize the average distance between each cluster’s centroid and the data points. This means that the data points within a cluster are close to each other, while the distance between clusters is maximized.<\/p>\n

2. How K-means Clustering Works<\/h2>\n
    \n
  1. Initialization:<\/strong> Randomly select K cluster centroids.<\/li>\n
  2. Assignment Step:<\/strong> Assign each data point to the nearest cluster centroid.<\/li>\n
  3. Update Step:<\/strong> Update the centroid of each cluster to the mean of the data points belonging to that cluster.<\/li>\n
  4. Convergence Check:<\/strong> If the change in cluster centroids is minimal or none, terminate the algorithm.<\/li>\n<\/ol>\n

    This process is repeated to find the optimal clusters.<\/p>\n

    3. Advantages and Disadvantages of K-means Clustering<\/h2>\n

    Advantages<\/h3>\n