Standard k-means clustering requires calculating the distance between every data point and every cluster centroid in every iteration. For large datasets (denoted as $n$) with high dimensionality (denoted as $d$), the complexity is $O(n \cdot k \cdot d \cdot i)$, where $k$ is the number of clusters and $i$ is the number of iterations.
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