This is an automated email from the ASF dual-hosted git repository. jiayu pushed a commit to branch branch-1.7.0 in repository https://gitbox.apache.org/repos/asf/sedona.git
commit d0ecaf785f09949254c6fed4824a7225bba4334a Author: Matthew Powers <[email protected]> AuthorDate: Fri Jan 24 21:01:27 2025 -0500 [DOCS] add sedona clustering algorithms page (#1772) * add sedona clustering algorithms page * lint images --- docs/image/tutorial/concepts/dbscan-clustering.png | Bin 0 -> 53848 bytes .../concepts/dbscan-scatterplot-points.png | Bin 0 -> 10969 bytes docs/tutorial/concepts/clustering-algorithms.md | 133 +++++++++++++++++++++ docs/tutorial/sql.md | 2 + mkdocs.yml | 2 + 5 files changed, 137 insertions(+) diff --git a/docs/image/tutorial/concepts/dbscan-clustering.png b/docs/image/tutorial/concepts/dbscan-clustering.png new file mode 100644 index 0000000000..8a839ddc01 Binary files /dev/null and b/docs/image/tutorial/concepts/dbscan-clustering.png differ diff --git a/docs/image/tutorial/concepts/dbscan-scatterplot-points.png b/docs/image/tutorial/concepts/dbscan-scatterplot-points.png new file mode 100644 index 0000000000..f5d45f90f4 Binary files /dev/null and b/docs/image/tutorial/concepts/dbscan-scatterplot-points.png differ diff --git a/docs/tutorial/concepts/clustering-algorithms.md b/docs/tutorial/concepts/clustering-algorithms.md new file mode 100644 index 0000000000..830b0667ee --- /dev/null +++ b/docs/tutorial/concepts/clustering-algorithms.md @@ -0,0 +1,133 @@ +<!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. + --> + +# Apache Sedona Clustering Algorithms + +Clustering algorithms group similar data points into “clusters.” Apache Sedona can run clustering algorithms on large geometric datasets. + +Note that the term cluster is overloaded here: + +* A computation cluster is a network of computers that work together to execute the algorithm +* A clustering algorithm divides data points into different “clusters” + +This page uses “cluster” to refer to the output of a clustering algorithm. + +## Clustering with DBSCAN + +This page explains how to use Apache Sedona to perform density-based spatial clustering of applications with noise (“DBSCAN”). + +This algorithm groups geometric objects in high-density areas as clusters and marks points in low-density areas as outliers. + +Let’s look at a scatter plot of points to visualize a data set that can be clustered. + + + +Here’s how the DBSCAN algorithm clusters the points: + + + +* 5 points are in cluster 0 +* 4 points are in cluster 1 +* 4 points are outliers + +Let’s create a Spark DataFrame with this data and then run the clustering with Sedona. Here’s how to construct the DataFrame: + +```python +df = ( + sedona.createDataFrame([ + (1, 8.0, 2.0), + (2, 2.6, 4.0), + (3, 2.5, 4.0), + (4, 8.5, 2.5), + (5, 2.8, 4.3), + (6, 12.8, 4.5), + (7, 2.5, 4.2), + (8, 8.2, 2.5), + (9, 8.0, 3.0), + (10, 1.0, 5.0), + (11, 8.0, 2.5), + (12, 5.0, 6.0), + (13, 4.0, 3.0), + ], ["id", "x", "y"]) +).withColumn("point", ST_Point(col("x"), col("y"))) +``` + +Here are the contents of the DataFrame: + +``` ++---+----+---+----------------+ +| id| x| y| point| ++---+----+---+----------------+ +| 1| 8.0|2.0| POINT (8 2)| +| 2| 2.6|4.0| POINT (2.6 4)| +| 3| 2.5|4.0| POINT (2.5 4)| +| 4| 8.5|2.5| POINT (8.5 2.5)| +| 5| 2.8|4.3| POINT (2.8 4.3)| +| 6|12.8|4.5|POINT (12.8 4.5)| +| 7| 2.5|4.2| POINT (2.5 4.2)| +| 8| 8.2|2.5| POINT (8.2 2.5)| +| 9| 8.0|3.0| POINT (8 3)| +| 10| 1.0|5.0| POINT (1 5)| +| 11| 8.0|2.5| POINT (8 2.5)| +| 12| 5.0|6.0| POINT (5 6)| +| 13| 4.0|3.0| POINT (4 3)| ++---+----+---+----------------+ +``` + +Here’s how to run the DBSCAN algorithm: + +```python +from sedona.stats.clustering.dbscan import dbscan + +dbscan(df, 1.0, 3).orderBy("id").show() +``` + +Here are the results of the computation: + +``` ++---+----+---+----------------+------+-------+ +| id| x| y| point|isCore|cluster| ++---+----+---+----------------+------+-------+ +| 1| 8.0|2.0| POINT (8 2)| true| 0| +| 2| 2.6|4.0| POINT (2.6 4)| true| 1| +| 3| 2.5|4.0| POINT (2.5 4)| true| 1| +| 4| 8.5|2.5| POINT (8.5 2.5)| true| 0| +| 5| 2.8|4.3| POINT (2.8 4.3)| true| 1| +| 6|12.8|4.5|POINT (12.8 4.5)| false| -1| +| 7| 2.5|4.2| POINT (2.5 4.2)| true| 1| +| 8| 8.2|2.5| POINT (8.2 2.5)| true| 0| +| 9| 8.0|3.0| POINT (8 3)| true| 0| +| 10| 1.0|5.0| POINT (1 5)| false| -1| +| 11| 8.0|2.5| POINT (8 2.5)| true| 0| +| 12| 5.0|6.0| POINT (5 6)| false| -1| +| 13| 4.0|3.0| POINT (4 3)| false| -1| ++---+----+---+----------------+------+-------+ +``` + +You can see the `cluster` column that indicates the grouping of the geometric object. + +To run this operation, you must set the Spark checkpoint directory. The checkpoint directory is a temporary cache in durable storage where the query's intermediate results are written. + +Here is how you can set the checkpoint directory: + +```python +sedona.sparkContext.setCheckpointDir(myPath) +``` + +`myPath` needs to be accessible to all executors. A local path is a good option on a local machine. When available, the HDFS is likely the best choice. Some runtime environments may allow or require block storage paths (e.g., Amazon S3, Google Cloud Storage). Depending on your environment, some runtime environments may already set the Spark checkpoint directory, so this step may not be necessary. diff --git a/docs/tutorial/sql.md b/docs/tutorial/sql.md index cb2297e789..d62026a4e8 100644 --- a/docs/tutorial/sql.md +++ b/docs/tutorial/sql.md @@ -885,6 +885,8 @@ The output will look like this: +----------------+---+------+-------+ ``` +See [this page](../concepts/clustering-algorithms) for more information on the DBSCAN algorithm. + ## Calculate the Local Outlier Factor (LOF) Sedona provides an implementation of the [Local Outlier Factor](https://en.wikipedia.org/wiki/Local_outlier_factor) algorithm to identify anomalous data. diff --git a/mkdocs.yml b/mkdocs.yml index 1a95eff243..876db88464 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -75,6 +75,8 @@ nav: - Examples: - Scala/Java: tutorial/demo.md - Python: tutorial/jupyter-notebook.md + - Concepts: + - Clustering Algorithms: tutorial/concepts/clustering-algorithms.md - API Docs: - Sedona with Apache Spark: - SQL:
