From e9eb68ab3832ec651264b967de709ee8ae41af5f Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Tue, 4 Jun 2024 23:28:27 +0530 Subject: [PATCH] Update K-Means_Clustering.md --- contrib/machine-learning/K-Means_Clustering.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index ab5fba9..c6aee6a 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -15,7 +15,6 @@ The K-means algorithm strategically assigns each data point to a cluster such th ## The Meaning Behind "K-Means" The "means" in K-means refers to the averaging process used to compute the centroid, essentially finding the center of each cluster. ## K-Means Algorithm in Action -![enter image description here](https://d3i71xaburhd42.cloudfront.net/2f49631bb3103a61fbc3045dd035c3d8f2175887/11-Figure2-1.png) The K-means algorithm follows an iterative approach to optimize cluster formation: 1. **Initial Centroid Placement:** The process begins with randomly selecting k centroids to serve as initial reference points for each cluster. @@ -66,13 +65,13 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio predicted_cluster = kmeans.predict(new_data_reshaped) print("Predicted cluster for new data:", predicted_cluster) - ### Output: - Before Implementing K-Means Clustering -![Before Implementing K-Means Clustering](https://miro.medium.com/v2/resize:fit:640/format:webp/1*jnyQxmEj7rFhazeMH7KXVg.png) + ### Output: +![Before Implementing K-Means Clustering](data:image/png;base64,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) - After Implementing K-Means Clustering - ![enter image description here](https://miro.medium.com/v2/resize:fit:640/format:webp/1*H3L3EH3Jh6kWFmbec0ewKA.png) + + ![After Implementing K-Means Clustering](data:image/png;base64,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) + Predicted cluster for new data: [0] ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation.