HDBSCAN difference between parameters

HDBSCAN: A Powerful Clustering Algorithm

HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a versatile and robust clustering algorithm widely used in various fields. It excels at identifying clusters of varying densities and shapes, even in the presence of noise.

One of the key strengths of HDBSCAN lies in its ability to be fine-tuned through parameters. Understanding these parameters and their impact on the clustering results is crucial for effective use of the algorithm.

Understanding Key Parameters

HDBSCAN offers a set of parameters that influence the clustering process. Let’s delve into some of the most important ones:

1. min_cluster_size: Minimum Size of Clusters

The min_cluster_size parameter sets the minimum number of data points required to form a cluster. This parameter effectively controls the sensitivity of the algorithm to smaller clusters.

• Higher values for min_cluster_size lead to the detection of larger and more prominent clusters, while ignoring smaller clusters.
• Lower values allow the algorithm to identify even smaller and less dense clusters, but might result in more noise being included in the clusters.

2. min_samples: Minimum Number of Samples

The min_samples parameter dictates the minimum number of data points required for a point to be considered a core point. Core points are essential for building the cluster hierarchy.

• A higher min_samples value increases the density threshold for core points, leading to fewer core points and potentially more isolated clusters.
• A lower min_samples value lowers the density threshold, resulting in more core points and possibly more interconnected clusters.

3. metric: Defining Similarity

The metric parameter determines the distance metric used to measure the similarity between data points.

• Common metrics include Euclidean distance, Manhattan distance, cosine similarity, and others.
• The choice of metric significantly impacts the clustering outcome, reflecting the specific characteristics of the data and the intended clustering objective.

Illustrative Example

Let’s consider a simple example to see how these parameters affect clustering results:

Code:

 import hdbscan import numpy as np # Sample data data = np.array([[1, 2], [1, 3], [1, 4], [2, 2], [2, 3], [2, 4], [5, 5], [5, 6], [5, 7]]) # Clustering with different parameters clusterer1 = hdbscan.HDBSCAN(min_cluster_size=2, min_samples=2) clusterer2 = hdbscan.HDBSCAN(min_cluster_size=4, min_samples=4) labels1 = clusterer1.fit_predict(data) labels2 = clusterer2.fit_predict(data) print("Clustering with min_cluster_size=2, min_samples=2:") print(labels1) print("\nClustering with min_cluster_size=4, min_samples=4:") print(labels2) 

Output:

 Clustering with min_cluster_size=2, min_samples=2: [0 0 0 0 0 0 1 1 1] Clustering with min_cluster_size=4, min_samples=4: [0 0 0 0 0 0 -1 -1 -1] 

In the first case (min_cluster_size=2, min_samples=2), the algorithm identifies two clusters. In the second case (min_cluster_size=4, min_samples=4), the algorithm identifies only one cluster, while classifying the remaining points as noise (denoted by -1).

Conclusion

By carefully selecting and adjusting these parameters, you can fine-tune HDBSCAN to achieve optimal clustering results for your specific data and application.