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Yess. Exactly. First we will calculate the distance of centroids with each data points. Then assign the clusters based on min value. After that we update the centroids.
The approach you're describing, where centroids are updated immediately after adding a row to a cluster, sounds like a form of online or incremental K-Means clustering, which differs from the traditional batch K-Means clustering algorithm.
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Nope after a new datapoint is added to a cluster new centroid if that cluster has to be calculated as it has a new data point in it and thus the centroid will keep changing
In the k-means clustering algorithm, the mean value (centroid) for each cluster is recalculated iteratively. The algorithm starts with an initial assignment of points to clusters and updates the centroids based on the mean of the points in each cluster. This process is repeated until convergence, where the assignment of points to clusters and the centroids no longer change significantly.
in certain scenarios or variations of k-means, there are adaptations that involve updating the means dynamically as new data points arrive. This is more common in online or streaming clustering algorithms. If you are working with a scenario where data points are added incrementally, and you want to update cluster means after each addition, you might be looking at an online clustering approach rather than the traditional k-means algorithm.
A very good video but has an error (updating centroid value before completion of an iteration). Correct method can be seen here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-KzJORp8bgqs.html
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Sir wow ap bhut hi acha pdhate ho. Sir please data science ke semester subjects ki bhi playlist layeiye. Like mongodb, oracle, python libraries pytorch, sklearn, and more. Thank u sir.❤
