K-Means Clustering (ML_CG_2)
This function groups similar data points into K clusters by iteratively assigning each data point to the nearest center and updating the cluster centers based on the mean of the assigned data points.
K-Means Clustering is a machine learning algorithm used for grouping similar data points into K clusters. It randomly selects K cluster centers and assigns each data point to the nearest center based on a distance metric. It then updates the cluster centers based on the mean of the assigned data points, and repeats the assignment and update steps until convergence.
The algorithm can converge to a local minimum, so it is often run multiple times with different initial cluster centers to improve the chances of finding the optimal solution.
Sample Request
Find the optimal number of clusters
POST
https://autogon.ai/api/v1/engine/start
Request Body
Name | Type | Description |
---|---|---|
project_id* | int | The |
parent_id* | int | The |
block_id* | int | The |
function_code* | string | The function code of the current block |
n_clusters* | int | numbers of iterations to make |
init* | string | initialization method to use |
random_state | String | control algorithm randomness |
Sample Request
Build a K-Means Clustering model named, "ClusterModel"
Building a K-Means Clustering model
K-Means Clustering
POST
https://autogon.ai/api/v1/engine/start
Request Body
Name | Type | Description |
---|---|---|
project_id* | int | The |
block_id* | int | The |
function_code* | string | The function code for current block |
parent_id* | int | The |
args* | object | Block arguments |
model_name* | String | Name of the model to be used for prediction. |
Sample Request
Make predictions with the pre-built model passing an optional test data.
Predicting with K-Means Clustering
K-Means Clustering Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
Name | Type | Description |
---|---|---|
model_name* | String | Name of previously trained model to be used for prediction |
test_data | String | Input data for prediction. Defaults to |
project_id* | int | ID of the current project |
block_id* | int | ID of the current block |
parent_id* | int | ID of the previous block |
function_code* | String | Function code for the current block |
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