DBSCAN is a density-based 🔠 Clustering algorithm that groups together similar data points and excludes outliers.
Notes
Sebastian Raschka
DBSCAN is a great alternative to K-Means when dealing with clusters of different densities or irregular shapes.
DBSCAN is an 🎓 Unsupervised Machine Learning algorithm for 🔠 Clustering data based on its density distribution. It groups together similar data points in high-density regions and excludes outliers. This technique is especially useful when dealing with clusters of varying density or shape, offering a great alternative to K-means.
TakeAways
- 📌 DBSCAN identifies high-density regions in data, grouping similar points together and excluding outliers
- 💡 This algorithm is an effective alternative to K-means clustering data of varying density or shape.
- 🔍 It’s a popular 🎓 Unsupervised Learning technique used for 🔠 Clustering
Process
- 📐 Calculate the density of each data point using its neighboring points within a specified distance (eps).
- 🤝 Group together nearby data points that meet the density threshold to form clusters.
- 💪 Mark any remaining, isolated data points as noise or outliers if they don’t belong to any cluster.