FAQ of ML: What are the main differences between K Nearest Neighbours (KNN) and K-Means Clustering?

K Nearest Neighbours (KNN) and K-Means Clustering are both fundamental algorithms in machine learning, but they serve different purposes and operate in distinct ways. Here are the main differences between the two:

Purpose:

• K Nearest Neighbours (KNN):

  • Type: Supervised learning algorithm.

  • Use Case: Classification and regression tasks.

  • Objective: Predict the class (or value) of a given data point based on the classes (or values) of its nearest neighbors.

• K-Means Clustering:

  • Type: Unsupervised learning algorithm.

  • Use Case: Clustering tasks.

  • Objective: Partition a set of data points into 𝑘 clusters, where each data point belongs to the cluster with the nearest mean.

Mechanism:

• K Nearest Neighbours (KNN):

  • Algorithm:

    1. Choose the number of neighbors 𝑘.

    2. Compute the distance between the query data point and all other points in the dataset.

    3. Select the 𝑘 nearest neighbors based on the computed distances.

    4. For classification, the predicted class is the majority class among the 𝑘 neighbors. For regression, the predicted value is the average of the 𝑘 neighbors’ values.

  • Distance Metric: Commonly uses Euclidean distance, but other metrics like Manhattan or Minkowski distance can also be used.

  • Training: Lazy learning algorithm, meaning it doesn’t build an explicit model during training. Instead, it stores the training data and performs computations during prediction.

• K-Means Clustering:

  • Algorithm:

    1. Choose the number of clusters 𝑘.

    2. Initialize 𝑘 cluster centroids, often randomly.

    3. Assign each data point to the nearest centroid, forming 𝑘 clusters.

    4. Recalculate the centroids as the mean of all points in each cluster.

    5. Repeat steps 3 and 4 until convergence (i.e., the assignments no longer change or the centroids stabilize).

  • Distance Metric: Typically uses Euclidean distance to determine the nearest centroid.

  • Training: Iterative algorithm that builds a model by updating cluster centroids until a convergence criterion is met.

Data Dependency:

• K Nearest Neighbours (KNN):

  • Dependency on Training Data: Heavily dependent, as it requires storing the entire training dataset for making predictions.

  • Scalability: Less scalable with large datasets due to the need to compute distances for all points in the dataset for each prediction.

• K-Means Clustering:

  • Dependency on Training Data: Less dependent once the model is trained, as the final centroids are used for new data points.

  • Scalability: Generally more scalable, especially with optimizations like mini-batch K-means.

Output:

• K Nearest Neighbours (KNN):

  • Output: Predicted class labels (for classification) or predicted values (for regression).

• K-Means Clustering:

  • Output: Cluster assignments for each data point and the final positions of the centroids.

Example Scenarios:

• K Nearest Neighbours (KNN):

  • Classification: Predicting whether an email is spam or not based on the similarity to previously labeled emails.

  • Regression: Predicting the price of a house based on features like size, location, and historical prices of similar houses.

• K-Means Clustering:

  • Clustering: Segmenting customers into distinct groups based on purchasing behavior for targeted marketing strategies.

  • Image Compression: Reducing the number of colors in an image by clustering pixel values.

In summary, KNN is a supervised learning algorithm used for classification and regression, relying heavily on the training data to make predictions. K-Means is an unsupervised learning algorithm used for clustering, aiming to partition the data into 𝑘 distinct groups.