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 <sowen@cloudera.com>

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

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