Difference between K-means and KNN

K-means is a clustering algorithm that partitions a dataset into K clusters by finding the centroids of each cluster, which represent the mean of the data points in that cluster. The goal is to minimize the distance between each data point and its assigned centroid. This can be visualized as data points being grouped together based on their similarity to the centroid.

KNN, on the other hand, is a classification algorithm that assigns a class to a test data point based on the classes of its K nearest neighbour in the training dataset. This can be visualized as the test data point being classified based on the classes of its nearest neighbours.

K-means and K-nearest neighbours (KNN) are two different machine learning algorithms with different goals and use cases.

K-means is a clustering algorithm that partitions a dataset into K clusters, where each data point belongs to the cluster with the nearest mean. It is an unsupervised learning algorithm, which means that it does not require labelled data to make predictions. K-means is often used for exploratory data analysis, customer segmentation, and image compression.

KNN, on the other hand, is a classification algorithm that finds the K nearest neighbours of a test data point and assigns it the most common class among those neighbours. It is a supervised learning algorithm, which means that it requires labelled data to make predictions. KNN is often used for image recognition, text classification, and recommender systems.

Here is an example of how K-means and KNN can be used for different applications:

K-means example: Suppose we have a dataset of customer transactions at a grocery store, and we want to group customers into different clusters based on their purchasing behaviour. We can use K-means to cluster the customers into K groups, where each group represents a different type of customer. We can then use this information to create targeted marketing campaigns for each group.

KNN example: Suppose we have a dataset of handwritten digits, and we want to build a machine-learning model to recognize them. We can use KNN to classify a test digit by finding the K nearest neighbours of the test digit in the training dataset and assigning it the most common class among those neighbours.

Summary

• K-means cluster data points based on their similarity to the centroid, while KNN classifies data points based on their similarity to the nearest neighbours.
• K-means and KNN are two different machine learning algorithms with different goals and use cases. K-means is a clustering algorithm used for unsupervised learning tasks, while KNN is a classification algorithm used for supervised learning tasks.

The comparison table is shown below for a better understanding of the difference between K-means and KNN.

Please keep in mind that the benefits and drawbacks described here are not fully addressed and may differ based on the individual use case and dataset.