K-Means Clustering

Example Usage

1from mltrain.unsupervised.KMeans import KMeans
2
3# Initialize the model
4model = KMeans(k=3, epochs=100)
5
6# Train the model
7cluster_labels = model.train(X_train)
8
9# The centroids can be accessed with
10centroids = model.centroids
11

Overview

The KMeans class implements the K-Means clustering algorithm. This algorithm partitions the dataset into k clusters, where each data point belongs to the cluster with the nearest centroid. The centroids are updated iteratively until convergence or until the maximum number of iterations is reached.

Hyperparameters

• k (default=3): The number of clusters to form.
• epochs (default=100): The maximum number of iterations for the algorithm.

Attributes

• centroids (numpy.ndarray): The coordinates of the centroids after training.

Methods

__init__(self, k=3, epochs=100)

Initializes the KMeans model with the specified parameters.

• Args:
  • k (int): The number of clusters to form.
  • epochs (int): The maximum number of iterations for the algorithm.

euclidean(self, x1, x2)

Computes the Euclidean distance between two points.

• Args:
  • x1 (numpy.ndarray): The first data point.
  • x2 (numpy.ndarray): The second data point.
• Returns:
  • float: The Euclidean distance between x1 and x2.

get_cluster_labels(self, clusters, X)

Assigns labels to each data point based on the cluster it belongs to.

• Args:
  • clusters (list of lists): A list of clusters, where each cluster is a list of indices of the data points.
  • X (numpy.ndarray): The dataset.
• Returns:
  • numpy.ndarray: An array of cluster labels for each data point in X.

closest_centroid(self, x1)

Finds the index of the closest centroid to the given data point.

• Args:
  • x1 (numpy.ndarray): The data point.
• Returns:
  • int: The index of the closest centroid.

create_clusters(self, X)

Creates clusters by assigning each data point to the closest centroid.

• Args:
  • X (numpy.ndarray): The dataset.
• Returns:
  • list of lists: A list of clusters, where each cluster is a list of indices of the data points.

create_new_centroids(self, clusters, X)

Updates the centroids by calculating the mean of all data points in each cluster.

• Args:
  • clusters (list of lists): A list of clusters, where each cluster is a list of indices of the data points.
  • X (numpy.ndarray): The dataset.
• Returns:
  • numpy.ndarray: An array of updated centroids.

train(self, X)

Trains the KMeans model by repeatedly assigning data points to the closest centroid and updating the centroids.

• Args:
  • X (numpy.ndarray): The dataset.
• Returns:
  • numpy.ndarray: An array of cluster labels for each data point in X.