From 33be4da5391b884191c405ffbce7d382ea8a2f66 Mon Sep 17 00:00:00 2001
From: Zheng RuiFeng <ruifengz@foxmail.com>
Date: Tue, 15 Nov 2016 15:44:50 +0100
Subject: [PATCH] [SPARK-18427][DOC] Update docs of mllib.KMeans
## What changes were proposed in this pull request?
1,Remove `runs` from docs of mllib.KMeans
2,Add notes for `k` according to comments in sources
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #15873 from zhengruifeng/update_doc_mllib_kmeans.
---
docs/mllib-clustering.md | 6 ++----
examples/src/main/python/mllib/k_means_example.py | 3 +--
2 files changed, 3 insertions(+), 6 deletions(-)
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index d5f6ae379a..8990e95796 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -24,13 +24,11 @@ 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 `spark.mllib` has the following parameters:
-* *k* is the number of desired clusters.
+* *k* is the number of desired clusters. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster.
* *maxIterations* is the maximum number of iterations to run.
* *initializationMode* specifies either random initialization or
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).
+* *runs* This param has no effect since Spark 2.0.0.
* *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.
* *initialModel* is an optional set of cluster centers used for initialization. If this parameter is supplied, only one run is performed.
diff --git a/examples/src/main/python/mllib/k_means_example.py b/examples/src/main/python/mllib/k_means_example.py
index 5c397e62ef..d6058f4502 100644
--- a/examples/src/main/python/mllib/k_means_example.py
+++ b/examples/src/main/python/mllib/k_means_example.py
@@ -36,8 +36,7 @@ if __name__ == "__main__":
parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
# Build the model (cluster the data)
- clusters = KMeans.train(parsedData, 2, maxIterations=10,
- runs=10, initializationMode="random")
+ clusters = KMeans.train(parsedData, 2, maxIterations=10, initializationMode="random")
# Evaluate clustering by computing Within Set Sum of Squared Errors
def error(point):
--
GitLab