Clustering algorithms are a vital part of machine learning, allowing us to group data points into clusters based on their similarities. In this section, we will explore some of the most common clustering algorithms.
Common Clustering Algorithms
1. K-Means
K-Means is one of the simplest and most widely used clustering algorithms. It divides the dataset into K clusters, where K is a predefined number. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroid positions.
2. Hierarchical Clustering
Hierarchical clustering creates a hierarchy of clusters. It can be agglomerative (bottom-up) or divisive (top-down). This method is useful when the number of clusters is not known beforehand.
3. DBSCAN
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. It identifies clusters based on the density of data points and can handle clusters of arbitrary shapes.
4. Mean-Shift
Mean-Shift is a non-parametric clustering technique that shifts the kernel window to the mean of the data points within the window. It is useful for finding clusters with arbitrary shapes and sizes.
Learn More
To delve deeper into clustering algorithms, check out our comprehensive guide on Clustering Techniques.
Visualizing Clustering Algorithms
Understanding the effectiveness of clustering algorithms is best achieved through visualization. Here is an image of K-Means clustering on a dataset:
For further reading on K-Means, you can explore our article on K-Means Clustering Explained.