The K-nearest neighbors and the k-means clustering algorithm are two of the most used machine learning algorithms. This article discusses the differences and similarities of the KNN vs KMeans algorithm.

Table of Contents

- KNN vs KMeans: Summary Table
- What is the KNN Algorithm?
- What is the K-means Clustering Algorithm?
- KNN vs KMeans: Similarities Between The Two Algorithms
- Knn vs KMeans: Differences Between The Two Algorithms
- KNN vs KMeans: What Should You Use?
- Conclusion

## KNN vs KMeans: Summary Table

If you want a quick snapshot of the differences between KNN and the K-means clustering algorithm, you can have a look at the following table.

KNN Algorithm | K-Means Algorithm |

We use the KNN algorithm for classification and regression tasks. | The KMeans algorithm is used for clustering. |

KNN classification is a supervised machine learning algorithm. | KMeans clustering is an unsupervised machine learning algorithm. |

To train a KNN model, we need a dataset with all the data points having class labels. | For training a K-means clustering model, we don’t need any such information. |

We use the KNN algorithm to predict the class label of a new data point. | We use the KMeans algorithm to find patterns in a given dataset by grouping data points into clusters. |

The KNN algorithm requires the choice of the number of nearest neighbors as its input parameter. | The KMeans clustering algorithm requires the number of clusters as an input parameter. |

Now, let us have a detailed discussion on KNN vs K-Means algorithm to understand these differences in a better manner.

## What is the KNN Algorithm?

K-Nearest Neighbors (KNN) is a simple but effective algorithm used in machine learning for classification and regression problems. The value of k is a hyperparameter that we can choose based on the characteristics of the data and the problem at hand.

The basic idea behind KNN is to classify new data points based on the classes of their k-nearest neighbors in the training dataset. In other words, when we give the algorithm a new data point to classify, it looks at the k nearest data points in the training set to the new data point. Then, it assigns the majority class label among those k neighbors to the new data point.

KNN works well on small datasets with a small number of features, but it can become computationally expensive for larger datasets. It also assumes that all features are equally important, which may not be the case in some applications.

To understand more about the KNN algorithm, you can read the following articles.

- KNN Classification Numerical example: This article discusses the basics of the KNN classification algorithm with a numerical example, its applications, advantages, and disadvantages.
- KNN Classification Using sklearn module in Python: This article discusses the implementation of the KNN classification algorithm in python using a sample dataset.
- KNN regression numerical example: This article discusses the basics of KNN regression with a numerical example, its applications, advantages, and disadvantages.
- KNN regression using the sklearn module in Python: This article discusses the implementation of the KNN regression algorithm in python using a sample dataset.
- KNN classification from scratch in Python: This article discusses the implementation of the KNN classification algorithm from scratch without using any in-built python libraries.

## What is the K-means Clustering Algorithm?

K-means is a popular unsupervised algorithm used for clustering in machine learning. This algorithm aims to partition a set of observations into k clusters, with each observation belonging to the cluster with the nearest mean or centroid.

The basic idea behind K-means is to start by randomly selecting k centroids from the data set. Here, k is the number of clusters we want to create. Then, we assign each data point to the nearest centroid, creating our initial clusters. Next, we update the centroids by taking the mean of all the data points in each cluster. We repeat the process of assigning data points to the nearest centroid and updating the centroids until the assignments no longer change, or until we reach the maximum number of iterations.

The K-means clustering algorithm can be sensitive to the initial choice of centroids and may converge to a local optimum instead of the global optimum. To overcome this, we need multiple runs of the algorithm with different initializations to find the best clusters with the highest cohesion.

To learn more about the K-means clustering algorithm, you can read the following articles.

- K-Means clustering numerical example: This article discusses the basics of k-means clustering with a numerical example, applications, advantages, and disadvantages.
- K-Means clustering using the sklearn module in Python: This article discusses the implementation of the k-means clustering algorithm using the sklearn module in Python.
- Elbow Method in Python for K-Means and K-Modes Clustering: This article discusses how to find the optimal number of clusters in k-means clustering using the elbow method.
- Silhouette Coefficient Approach in Python For K-Means Clustering: This article discusses the implementation of the silhouette coefficient approach to find the optimal number of clustering in k-means clustering.

By now, you must have understood the basics of k-means and the KNN algorithm. Let us now discuss the similarities and differences between the two algorithms.

## KNN vs KMeans: Similarities Between The Two Algorithms

KNN (K-Nearest Neighbors) and K-means clustering are used for entirely different tasks. However, there are a few similarities between the two algorithms as well.

**Both KNN and K-means are iterative algorithms**. In the K-means clustering algorithm, we need to iteratively choose centroids and assign points to different clusters. We do this until a number of iterations or until a situation where the centroids don’t change in two consecutive iterations. Therefore, we often need two or more iterations in K-means clustering. In KNN, we can find class labels for a new data point in a single iteration. Here, instead of iterating the whole process, we use iteration to find the distance between the new data point and the existing data points to find the nearest neighbors.**KNN and K-means algorithms use distance metrics to analyze the data.**Both the KNN and K-means algorithms use distance metrics such as euclidean distance, manhattan distance, or Minkowski distance. The KNN algorithm uses a distance metric to measure the similarity between a new data point and existing data points. On the other hand, the K-means algorithm uses a distance metric to measure the similarity between the data points and the centroids.

## Knn vs KMeans: Differences Between The Two Algorithms

Despite the similarities discussed in the previous section, KNN, and K-means algorithms are fundamentally different. KNN is a supervised learning algorithm used for classification and regression. On the contrary, K-means is an unsupervised learning algorithm used for clustering. Let us discuss some of the differences between the KNN and K-means clustering algorithms.

**Objective**: We use the KNN algorithm for classification and regression tasks. The K-Means algorithm is used for clustering.**Supervision**: KNN is a supervised machine learning algorithm. KMeans is an unsupervised machine learning algorithm.**Input**: To train a KNN model, we need a dataset with all the data points having class labels. For training a K-means clustering model, we don’t need any such information.**Output**: We use the KNN algorithm to predict the class label of a new data point. On the other hand, we use the KMeans algorithm to find patterns in a given dataset by grouping data points into clusters.**Parameter**: The KNN algorithm requires the choice of the number of nearest neighbors as its input parameter. The KMeans clustering algorithm requires the number of clusters as an input parameter.

## KNN vs KMeans: What Should You Use?

KNN is a supervised learning algorithm used for classification and regression problems. K-Means, on the other hand, is an unsupervised learning algorithm used for clustering problems. Therefore, the choice between KNN and K-Means depends on the nature of the problem you are trying to solve.

- If you have labeled data and you want to classify or predict the labels of new data points, then KNN would be a more appropriate algorithm for you.
- If you have unlabeled data and you want to group them into similar clusters to find patterns in the data, then K-Means would be more suitable.

## Conclusion

In this article, we have discussed the similarities and differences between the KNN vs KMeans clustering algorithm. To learn more about machine learning, you can read this article on market basket analysis in data mining. You might also like this article on how to find clusters from a dendrogram in python.

I hope you enjoyed reading this article. Stay tuned for more informative articles.

Happy Learning!