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finish docs for kmeans nonspatial

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Andy Eschbacher 2017-01-10 10:43:42 -05:00
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doc/11_kmeans.md
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### CDB_KMeans(subquery text, no_clusters INTEGER)
This function attempts to find n clusters within the input data. It will return a table to CartoDB ids and
the number of the cluster each point in the input was assigend to.
This function attempts to find `no_clusters` clusters within the input data based on the geographic distribution. It will return a table with ids and the cluster classification of each point input assuming `the_geom` is not null-valued. If `the_geom` is null-valued, the point will not be considered in the analysis.
#### Arguments
@ -19,17 +17,19 @@ A table with the following columns.
| Column Name | Type | Description |
|-------------|------|-------------|
| cartodb\_id | INTEGER | The CartoDB id of the row in the input table.|
| cluster\_no | INTEGER | The cluster that this point belongs to. |
| cartodb\_id | INTEGER | The row id of the row from the input table |
| cluster\_no | INTEGER | The cluster that this point belongs to |
#### Example Usage
```sql
SELECT
customers.*,
km.cluster_no
FROM cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) km, customers_3
SELECT
customers.*,
km.cluster_no
FROM
cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) As km,
customers
WHERE customers.cartodb_id = km.cartodb_id
```
@ -37,7 +37,7 @@ SELECT
Function that computes the weighted centroid of a number of clusters by some weight column.
### Arguments
### Arguments
| Name | Type | Description |
|------|------|-------------|
@ -45,18 +45,57 @@ Function that computes the weighted centroid of a number of clusters by some wei
| weight\_column | TEXT | The name of the column to use as a weight |
| category\_column | TEXT | The name of the column to use as a category |
### Returns
### Returns
A table with the following columns.
| Column Name | Type | Description |
|-------------|------|-------------|
| the\_geom | GEOMETRY | A point for the weighted cluster center |
| class | INTEGER | The cluster class |
| class | INTEGER | The cluster class |
### Example Usage
### Example Usage
```sql
SELECT ST_TRANSFORM(the_geom, 3857) as the_geom_webmercator, class
FROM cdb_weighted_mean('SELECT *, customer_value FROM customers','customer_value','cluster_no')
```sql
SELECT
ST_Transform(the_geom, 3857) As the_geom_webmercator,
class
FROM
cdb_crankshaft.CDB_Weighted_Mean(
'SELECT *, customer_value FROM customers',
'customer_value',
'cluster_no')
```
## CDB_KMeansNonspatial(subquery text, colnames text[], no_clusters int)
K-means clustering classifies the rows of your dataset into `no_clusters` by finding the centers (means) of the variables in `colnames` and classifying each row by it's proximity to the nearest center. This method partitions space into distinct Voronoi cells.
As a standard machine learning method, k-means clustering is an unsupervised learning technique that finds the natural clustering of values. For instance, it is useful for finding subgroups in census data leading to demographic segmentation.
### Arguments
| Name | Type | Description |
|------|------|-------------|
| query | TEXT | SQL query to expose the data to be used in the analysis (e.g., `SELECT * FROM iris_data`). It should contain at least the columns specified in `colnames` and the `id_colname`. |
| colnames | TEXT[] | Array of columns to be used in the analysis (e.g., `Array['petal_width', 'sepal_length', 'petal_length']`). |
| no\_clusters | INTEGER | Number of clusters for the classification of the data |
| id_colname (optaional) | TEXT | The id column (default: 'cartodb_id') for identifying rows |
| standarize (optional) | BOOLEAN | Setting this to true (default) standardizes the data to have a mean at zero and a standard deviation of 1 |
### Returns
A table with the following columns.
| Column | Type | Description |
|--------|------|-------------|
| cluster_label | TEXT | Label that a cluster belongs to, number from 0 to `no_clusters - 1`. |
| cluster_center | JSON | Center of the cluster that a row belongs to. The keys of the JSON object are the `colnames`, with values that are the center of the respective cluster |
| silhouettes | NUMERIC | [Silhouette score](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score) of the cluster label |
| rowid | BIGINT | id of the original row for associating back with the original data |
### Resources
- Read more in [scikit-learn's documentation](http://scikit-learn.org/stable/modules/clustering.html#k-means)
- [K-means basics](https://www.datascience.com/blog/introduction-to-k-means-clustering-algorithm-learn-data-science-tutorials)