{"id":2148,"date":"2025-09-07T20:05:54","date_gmt":"2025-09-07T19:05:54","guid":{"rendered":"https:\/\/cyberenlightener.com\/?page_id=2148"},"modified":"2025-10-04T10:16:53","modified_gmt":"2025-10-04T09:16:53","slug":"unsupervised-learning","status":"publish","type":"page","link":"https:\/\/cyberenlightener.com\/?page_id=2148","title":{"rendered":"Unsupervised learning"},"content":{"rendered":"\n

K-MEANS CLUSTERING<\/strong><\/h2>\n\n\n\n

K-Means<\/strong>\u00a0clustering is a powerful and efficient algorithm for grouping data points into clusters based on similarity. Despite its simplicity, it is widely used in various fields due to its scalability, ease of use, and effectiveness in uncovering patterns in large datasets. However, it is important to consider its limitations, such as sensitivity to initialization and the need for a predefined number of clusters. Despite these challenges, K-Means remains one of the most popular clustering algorithms in data analysis and machine learning.<\/p>\n\n\n\n

Excercise-1<\/a><\/strong><\/p>\n\n\n\n

Implement the K-Means algorithm with K = 2 on the data points (185, 72), (170, 56), (168, 60), (179, 68), (182, 72), and (188, 77) for two iterations, and display the resulting clusters. Initially, select the first two data points as the initial centroids.<\/p>\n\n\n\n

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In the\u00a0initial step,\u00a0<\/strong>we determine the similarity between data points using the Euclidean distance metric.<\/p>\n\n\n\n

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In tabular-form it can be represented as,<\/p>\n\n\n\n

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The result after first iteration.<\/p>\n\n\n\n

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In the second iteration, calculating centroids again,<\/p>\n\n\n\n

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Calculating distances again,<\/p>\n\n\n\n

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As two iterations have already been completed as required by the problem, the numerical process concludes here. Since the clustering remains unchanged after the second iteration, the process will be terminated, even if the question does not explicitly state to do so.<\/p>\n\n\nRead more: Unsupervised learning<\/span><\/a>\n\n\n

K-MODES CLUSTERING<\/strong><\/h2>\n\n\n\n