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Alex Rivera
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The k-means++ algorithm helps in two following points of the original k-means algorithm: The original k-means algorithm has the worst case running time of super-polynomial in input size, while k-means++ has claimed to be O(log k). The approximation found can yield a not so satisfactory result with respect to objective function compared to the optimal clustering. But are there any drawbacks of k-means++? Should we always used it instead of k-means from now on?
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