k-mean clustering · what is k-mean clustering k-mean clustering is an iterative algorithm that...
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K-MEAN Clustering
Dr. Fizar Ahmed
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What is k-mean clustering
K-mean clustering is an iterative algorithm that tries to partition the dataset into K pre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group.
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The way kmeans algorithm works is as follows:
● Specify number of clusters K.● Initialize centroids by first shuffling the dataset and then randomly
selecting K data points for the centroids without replacement.● Keep iterating until there is no change to the centroids. i.e
assignment of data points to clusters isn’t changing.● Compute the sum of the squared distance between data points and
all centroids.● Assign each data point to the closest cluster (centroid).● Compute the centroids for the clusters by taking the average of the
all data points that belong to each cluster.
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Flowchart of k-means clustering algorithm
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Application of K-MEAN Clustering
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Thank you