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author | wm624@hotmail.com <wm624@hotmail.com> | 2016-05-16 08:22:16 +0200 |
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committer | Nick Pentreath <nickp@za.ibm.com> | 2016-05-16 08:22:16 +0200 |
commit | c1836d66bdc93f80ff9e8852efe8f2d2bc1ca941 (patch) | |
tree | f690c5a5e1c2d8605acfda4876fa7a673762dc65 /docs | |
parent | 4a5ee1954a6fb77231abb492355fe70313f0b35b (diff) | |
download | spark-c1836d66bdc93f80ff9e8852efe8f2d2bc1ca941.tar.gz spark-c1836d66bdc93f80ff9e8852efe8f2d2bc1ca941.tar.bz2 spark-c1836d66bdc93f80ff9e8852efe8f2d2bc1ca941.zip |
[SPARK-15305][ML][DOC] spark.ml document Bisectiong k-means has the incorrect format
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
The generated document has the incorrect format for biseckmeans.
![bug](https://cloud.githubusercontent.com/assets/5033592/15233120/d910098a-185a-11e6-901d-44aeafc8a011.jpg)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Fix the formatting.
![fix](https://cloud.githubusercontent.com/assets/5033592/15233136/fce2ccd0-185a-11e6-9ded-14d71da4bdab.jpg)
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes #13083 from wangmiao1981/doc.
Diffstat (limited to 'docs')
-rw-r--r-- | docs/ml-clustering.md | 4 |
1 files changed, 1 insertions, 3 deletions
diff --git a/docs/ml-clustering.md b/docs/ml-clustering.md index 0d69bf67df..a0955a3855 100644 --- a/docs/ml-clustering.md +++ b/docs/ml-clustering.md @@ -86,7 +86,6 @@ Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering. </div> </div> - ## Latent Dirichlet allocation (LDA) `LDA` is implemented as an `Estimator` that supports both `EMLDAOptimizer` and `OnlineLDAOptimizer`, @@ -116,8 +115,8 @@ Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering. {% include_example python/ml/lda_example.py %} </div> </div> -## Bisecting k-means +## Bisecting k-means Bisecting k-means is a kind of [hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) using a divisive (or "top-down") approach: all observations start in one cluster, and splits are performed recursively as one @@ -148,5 +147,4 @@ Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering. {% include_example python/ml/bisecting_k_means_example.py %} </div> - </div> |