{"id":136300,"date":"2024-09-20T11:05:57","date_gmt":"2024-09-20T05:35:57","guid":{"rendered":"https:\/\/www.vskills.in\/certification\/tutorial\/?page_id=136300"},"modified":"2024-09-20T11:05:58","modified_gmt":"2024-09-20T05:35:58","slug":"step-by-step-visualization-of-k-means","status":"publish","type":"page","link":"https:\/\/www.vskills.in\/certification\/tutorial\/step-by-step-visualization-of-k-means\/","title":{"rendered":"Step-by-step visualization of K-Means"},"content":{"rendered":"\n

K-means clustering is a popular unsupervised learning algorithm that partitions a dataset into K distinct clusters. Visualizing the K-means process can help us understand how the algorithm works and how it converges to a solution.<\/p>\n\n\n\n

Understanding the K-Means Algorithm<\/strong><\/h3>\n\n\n\n

Before we delve into the visualization, let’s recap the key steps of the K-means algorithm:<\/p>\n\n\n\n

    \n
  1. Initialization:<\/strong> Randomly select K data points as initial centroids.<\/li>\n\n\n\n
  2. Assignment:<\/strong> Assign each data point to the nearest centroid based on Euclidean distance. \u00a0<\/li>\n\n\n\n
  3. Update:<\/strong> Calculate the new centroids as the mean of all data points assigned to each cluster.<\/li>\n\n\n\n
  4. Repeat:<\/strong> Iterate steps 2 and 3 until the centroids converge or a maximum number of iterations is reached.<\/li>\n<\/ol>\n\n\n\n

    Visualization Steps<\/strong><\/h3>\n\n\n\n

    To visualize the K-means process, we can plot the data points and the centroids at each iteration. This will help us observe how the centroids move and how the clusters are formed.<\/p>\n\n\n\n

    Step 1: Initialization<\/strong><\/p>\n\n\n\n

      \n
    • Randomly select K data points as initial centroids.<\/li>\n\n\n\n
    • Plot the data points and the initial centroids on a scatter plot.<\/li>\n<\/ul>\n\n\n\n

      Step 2: Assignment<\/strong><\/p>\n\n\n\n

        \n
      • Calculate the Euclidean distance between each data point and each centroid.<\/li>\n\n\n\n
      • Assign each data point to the nearest centroid.<\/li>\n\n\n\n
      • Color-code the data points based on their assigned clusters.<\/li>\n<\/ul>\n\n\n\n

        Step 3: Update<\/strong><\/p>\n\n\n\n

          \n
        • Calculate the new centroids as the mean of all data points assigned to each cluster.<\/li>\n\n\n\n
        • Plot the updated centroids on the scatter plot.<\/li>\n<\/ul>\n\n\n\n

          Step 4: Repeat<\/strong><\/p>\n\n\n\n

            \n
          • Repeat steps 2 and 3 until convergence or a maximum number of iterations is reached.<\/li>\n\n\n\n
          • At each iteration, update the plot to show the new cluster assignments and centroid positions.<\/li>\n<\/ul>\n\n\n\n

            Example Visualization<\/strong><\/h3>\n\n\n\n

            Consider a dataset of two-dimensional points:<\/p>\n\n\n\n

            Data points: (2, 3), (4, 5), (6, 7), (8, 9), (1, 2)\nK = 2\n<\/code><\/pre>\n\n\n\n
              \n
            1. Initialization:<\/strong> Randomly select two data points as initial centroids: (2, 3) and (8, 9).<\/li>\n\n\n\n
            2. Assignment:<\/strong>\n
                \n
              • (2, 3) is closer to (2, 3) than (8, 9), so it is assigned to cluster 1.<\/li>\n\n\n\n
              • (4, 5) is closer to (2, 3) than (8, 9), so it is assigned to cluster 1.<\/li>\n\n\n\n
              • (6, 7) is closer to (8, 9) than (2, 3), so it is assigned to cluster 2.<\/li>\n\n\n\n
              • (8, 9) is closer to (8, 9) than (2, 3), so it is assigned to cluster 2.<\/li>\n\n\n\n
              • (1, 2) is closer to (2, 3) than (8, 9), so it is assigned to cluster 1.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
              • Update:<\/strong>\n
                  \n
                • Centroid of cluster 1: (2 + 4 + 1) \/ 3 = 2.33, (3 + 5 + 2) \/ 3 = 3.33<\/li>\n\n\n\n
                • Centroid of cluster 2: (6 + 8) \/ 2 = 7, (7 + 9) \/ 2 = 8<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                • Repeat:<\/strong> Continue iterating steps 2 and 3, updating the plot at each iteration, until convergence or a maximum number of iterations is reached.<\/li>\n<\/ol>\n\n\n\n
                  By visualizing the K-means process in this way, we can gain a better understanding of how the algorithm converges to a solution and how the clusters are formed.<\/p>\n","protected":false},"excerpt":{"rendered":"
                  K-means clustering is a popular unsupervised learning algorithm that partitions a dataset into K distinct clusters. Visualizing the K-means process can help us understand how the algorithm works and how it converges to a solution. Understanding the K-Means Algorithm Before we delve into the visualization, let’s recap the key steps of the K-means algorithm: Visualization…<\/p>\n","protected":false},"author":16,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-136300","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nStep-by-step visualization of K-Means - Tutorial<\/title>\n<meta name=\"description\" content=\"Follow this step-by-step visualization of K-Means clustering. 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