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### Advantages

1) Fast, robust, and easier to understand. 2) Relatively efficient: O(t*k*n*d), where n is # objects, k is # clusters, d is # dimension of each object, and t is # iterations. Normally, k, t, d << n. 3) Gives the best result when the data set is distinct or well separated from each other.### Disadvantages

1) The use of Exclusive Assignment - If there are two highly overlapping data then k-means will not be able to resolve that there are two clusters. 2) The learning algorithm is not invariant to non-linear transformations i.e. with a different representation of data we get different results (data represented in form of cartesian co-ordinates and polar co-ordinates will give different results). 3) Euclidean distance measures can unequally weigh underlying factors. 4) The learning algorithm provides the local optima of the squared error function.#### References

- https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a#:~:text=Kmeans%20algorithm%20is%20an%20iterative,belongs%20to%20only%20one%20group.&text=The%20less%20variation%20we%20have,are%20within%20the%20same%20cluster.
- https://sites.google.com/site/dataclusteringalgorithms/k-means-clustering-algorithm
- https://en.wikipedia.org/wiki/K-means_clustering
- https://www.geeksforgeeks.org/k-means-clustering-introduction/
- https://www.analyticsvidhya.com/blog/2020/10/a-simple-explanation-of-k-means-clustering/