From 25ad8f93012730115a8a1fac649fe3e842c045b3 Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Tue, 6 May 2014 20:07:22 -0700 Subject: SPARK-1727. Correct small compile errors, typos, and markdown issues in (primarly) MLlib docs While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs. Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown. Author: Sean Owen Closes #653 from srowen/SPARK-1727 and squashes the following commits: 6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count 8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output) 99966a9 [Sean Owen] Update issue tracker URL in docs 23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak) 8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs --- docs/mllib-clustering.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'docs/mllib-clustering.md') 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 -- cgit v1.2.3