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## What changes were proposed in this pull request? 1, add BisectingKMeans to ml-clustering.md 2, add the missing Scala BisectingKMeansExample 3, create a new datafile `data/mllib/sample_kmeans_data.txt` ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #11844 from zhengruifeng/doc_bkm.
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| layout | title | displayTitle |
|---|---|---|
| global | Clustering - spark.ml | Clustering - spark.ml |
In this section, we introduce the pipeline API for clustering in mllib.
Table of Contents
- This will become a table of contents (this text will be scraped). {:toc}
K-means
k-means is one of the most commonly used clustering algorithms that clusters the data points into a predefined number of clusters. The MLlib implementation includes a parallelized variant of the k-means++ method called kmeans||.
KMeans is implemented as an Estimator and generates a KMeansModel as the base model.
Input Columns
| Param name | Type(s) | Default | Description |
|---|---|---|---|
| featuresCol | Vector | "features" | Feature vector |
Output Columns
| Param name | Type(s) | Default | Description |
|---|---|---|---|
| predictionCol | Int | "prediction" | Predicted cluster center |
Example
{% include_example scala/org/apache/spark/examples/ml/KMeansExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaKMeansExample.java %}
Latent Dirichlet allocation (LDA)
LDA is implemented as an Estimator that supports both EMLDAOptimizer and OnlineLDAOptimizer,
and generates a LDAModel as the base models. Expert users may cast a LDAModel generated by
EMLDAOptimizer to a DistributedLDAModel if needed.
Refer to the Scala API docs for more details.
{% include_example scala/org/apache/spark/examples/ml/LDAExample.scala %}
Refer to the Java API docs for more details.
{% include_example java/org/apache/spark/examples/ml/JavaLDAExample.java %}
Bisecting k-means
Bisecting k-means is a kind of hierarchical clustering using a divisive (or "top-down") approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
Bisecting K-means can often be much faster than regular K-means, but it will generally produce a different clustering.
BisectingKMeans is implemented as an Estimator and generates a BisectingKMeansModel as the base model.
Example
{% include_example scala/org/apache/spark/examples/ml/BisectingKMeansExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java %}
{% include_example python/ml/bisecting_k_means_example.py %}