Explain K-means clustering methods with the help of a suitable example. Also, discuss the advantages and disadvantages of k -means clustering methods.

K-means clustering is an unsupervised machine learning algorithm commonly used in digital image processing and computer vision for partitioning data into k distinct clusters based on similarity. It aims to minimize the variance within each cluster and works iteratively to assign each data point to one of k clusters based on the nearest mean (centroid).

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