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 used to partition data into K distinct clusters based on feature similarity. The algorithm works by minimizing the variance within each cluster. It involves the following steps:

1. Initialization: Choose K random centroids (initial cluster centers) from the dataset.

2. Assignment: Each data point is assigned to the nearest centroid based on the Euclidean distance.

3. Update: After assignment, the centroids are recalculated _____ _______ ________ _____ __________ _________ ____.
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