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

// Some code

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