k-means clustering

Mastering K-Means Clustering: A Comprehensive Guide

K-means clustering algorithm is one of those algorithms that play a crucial role in data analysis.

Furthermore, in this article, we’ll delve into its applications, and how to analyze its results effectively.

K-Means Clustering: A Simple Introduction for Beginners

It’s is an unsupervised learning algorithm that divides a dataset into k distinct clusters based on the similarity between data points.

Furthermore, it aims to minimize the sum of squared distances within each cluster, resulting in tight, cohesive groups.

How Does K-Means Clustering Work in Machine Learning?

For further referrence, centroid is the center data point of a cluster. Moreover, the whole algorithm revolves around these centroids.

The algorithm works iteratively by following these steps:

  1. Initialize number of clusters “k” centroids randomly.
  2. Assign each data point to the nearest centroid.
  3. Update the centroids by calculating the mean of all points in each cluster.
  4. Repeat steps 2 and 3 until convergence or a specified number of iterations.

Best Use Cases of K-Means Clustering

  • You have a large dataset with continuous features.
  • You know or are able to estimate the number of clusters (k).
  • If you expect the clusters to be of similar size and shape.

K-Means for Classification: A Two-Step Process

Though k-means is primarily a clustering algorithm, we can use it for classification by following these steps:

  1. Perform k-means clustering to create groups.
  2. Label each cluster based on the majority class of its data points.

Essentially, we can use it to create labeled data, which we can use it to train a machine learning model to perform a classification task.

Analyzing Results

To evaluate the quality of your clustering results, consider the following:

  1. Within-cluster sum of squares (WCSS): Aim for a low WCSS, indicating tight clusters.
  2. Silhouette score: A measure of cluster cohesion and separation. Higher scores indicate better results.
  3. Visual analysis: Plot your clusters to identify any overlaps or unusual patterns.

Difference Between KNN and K-Means

KNN (k-nearest neighbors) is a supervised learning algorithm we can use for classification and regression.

K-means, on the other hand, is an unsupervised learning algorithm for clustering. While KNN predicts the class of a new data point based on its nearest neighbors, k-means groups data points based on their similarity.


In conclusion, k-means clustering is a powerful and versatile technique for identifying patterns in your data.

Understanding its inner workings and applications can help you make informed decisions and build more accurate, insightful models.

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