Comment on Toward Increased k-means Clustering Efficiency with the Naive Sharding Centroid Initialization Method by https://thoitiet.online/vinh-long/
The k-means algorithm is a simple yet effective approach to clustering. k points are (usually) randomly chosen as cluster centers, or centroids, and all dataset instances are plotted and added to the closest cluster. After all instances have been added to clusters, the centroids, representing the mean of the instances of each cluster are re-calculated, with these re-calculated centroids becoming the new centers of their respective clusters. Cluster membership is then reset, and the new centroids are used to re-plot and all instances and add them to their closest centroid cluster.
- ▪The k-means algorithm is a simple yet effective approach to clustering. k points are (usually) randomly chosen as cluster centers, or centroids, and all dataset instances are plotted and added to the closest cluster.
- ▪After all instances have been added to clusters, the centroids, representing the mean of the instances of each cluster are re-calculated, with these re-calculated centroids becoming the new centers of their respective clusters.
- ▪Cluster membership is then reset, and the new centroids are used to re-plot and all instances and add them to their closest centroid cluster.
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| Original publisher | KDnuggets » Comments Feed |
| Canonical URL | https://www.kdnuggets.com/2017/03/naive-sharding-centroid-initialization-method.html#comment-182588 |
| Publication time | Fri, 24 Jul 2026 23:30:25 +0000 |
| Retrieval time | 2026-07-25T01:12:55.855Z |
| Last seen | 2026-07-25T01:12:55.855Z |
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Opening excerpt (first ~120 words) tap to expand
The k-means algorithm is a simple yet effective approach to clustering. k points are (usually) randomly chosen as cluster centers, or centroids, and all dataset instances are plotted and added to the closest cluster. After all instances have been added to clusters, the centroids, representing the mean of the instances of each cluster are re-calculated, with these re-calculated centroids becoming the new centers of their respective clusters. Cluster membership is then reset, and the new centroids are used to re-plot and all instances and add them to their closest centroid cluster. This iterative process continues until convergence -- centroid locations remain constant between successive clustering iterations -- is reached.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at KDnuggets » Comments Feed.