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

{
    "project_id": 12,
    "parent_id": 2,
    "block_id": 3,
    "function_code": "ML_CG_2_F",
    "args": {
        "n_clusters": 11,
        "init": "k-means++",
        "random_state": 42
    }
}

POST https://autogon.ai/api/v1/engine/start

Request Body

Name	Type	Description
project_id*	int	The id of the current project
parent_id*	int	The id of the previous block
block_id*	int	The id of the current block
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

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8


kmeansClusteringFindClusters   = await client.kmeans_clustering_find_clusters(project_id, parent_id, block_id, {
    n_clusters: 11,
    init: "k-means++",
    random_state: 42
});

Sample Request

Build a K-Means Clustering model named, "ClusterModel"

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "ML_CG_2",
    "args": {
        "model_name": "ClusterModel",
        "n_clusters": 11,
        "init": "k-means++",
        "random_state": 42
    }
}

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 id of the current project
block_id*	int	The id of the current block
function_code*	string	The function code for current block
parent_id*	int	The id of the previous block
args*	object	Block arguments
model_name*	String	Name of the model to be used for prediction.

200 Statemanagement object

{
    "status": "true",
    "message": {
        "id": 8,
        "project": 1,
        "block_id": 8,
        "parent_id": 7,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"ClusterModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
    }
}

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8

kmeansClustering = await client.kmeans_clustering(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
    n_clusters: 5,
    affinity: "euclidean",
    linkage: "ward"
});

Sample Request

Make predictions with the pre-built model passing an optional test data.

{
    "project_id": 1,
    "parent_id": 9,
    "block_id": 10,
    "function_code": "ML_CG_2_P",
    "args": {
        "model_name": "ClusterModel",
        "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 x_train_url in StateManagment
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

200: OK Statemanagement object

{
    "status": "true",
    "message": {
        "id": 9,
        "project": 1,
        "block_id": 9,
        "parent_id": 8,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": "{\"y_pred_url\": ""}"
    }
}

Python

// Some code

Node

const project_id = 1
const parent_id = 7
const block_id = 8

kmeansClusteringPredict = await client.kmeans_clustering_predict(project_id, parent_id, block_id, {
    model_name: "SimpleModel",
    test_data: ""
});