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Diffstat (limited to 'docs/mllib-clustering.md')
| -rw-r--r-- | docs/mllib-clustering.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index b3293afe40..276868fa84 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -18,7 +18,7 @@ models are trained for each cluster). MLlib supports [k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, one of the most commonly used clustering algorithms that clusters the data points into -predfined number of clusters. The MLlib implementation includes a parallelized +predefined number of clusters. The MLlib implementation includes a parallelized variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf). The implementation in MLlib has the following parameters: @@ -30,7 +30,7 @@ initialization via k-means\|\|. * *runs* is the number of times to run the k-means algorithm (k-means is not guaranteed to find a globally optimal solution, and when run multiple times on a given dataset, the algorithm returns the best clustering result). -* *initializiationSteps* determines the number of steps in the k-means\|\| algorithm. +* *initializationSteps* determines the number of steps in the k-means\|\| algorithm. * *epsilon* determines the distance threshold within which we consider k-means to have converged. ## Examples |