Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

by Sebastian Raschka

However, we shall also note some of the disadvantages of DBSCAN. With an increasing number of features in our dataset—assuming a fixed number of training examples—the negative effect of the curse of dimensionality increases. This is especially a problem if we are using the Euclidean distance metric. However, the problem of the curse of dimensionality is not unique to DBSCAN; it also affects other clustering algorithms that use the Euclidean distance metric, for example, k-means and hierarchical clustering algorithms. In addition, we have two hyperparameters in DBSCAN (MinPts and ) that need to be optimized to yield good clustering results. Finding a good combination of MinPts and can be problematic if the density differences in the dataset are relatively large.