I have made a basic HDBSCAN model (picture output below) but I need to figure out names for the individual clusters. Is there a way I can get something like a decision tree or the parameters for each cluster as that is what I need to classify these clusters properly. For example: category -1 has to have COMP_2_Pass = 1 and REBOUND_3 < -2300 or something that shows filtering logic like this. Thanks GRAPH import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import umap.umap_ as umap import hdbscan from sklearn.preprocessing import StandardScaler #Drop label column X = df_cleaned.drop(columns=['Failure_area']) #Scale features X_scaled = StandardScaler().fit_transform(X) #UMAP for 2D projection reducer = umap.UMAP(random_state=42) X_umap = reducer.fit_transform(X_scaled) # HDBSCAN clustering clusterer = hdbscan.HDBSCAN(min_cluster_size=40, min_samples= 55) cluster_labels = clusterer.fit_predict(X_scaled) # Add results to DataFrame df_clustered = df_cleaned.copy() df_clustered['HDBSCAN_cluster'] = cluster_labels from matplotlib.colors import ListedColormap # Get unique cluster labels unique_labels = np.unique(cluster_labels) n_clusters = len(unique_labels) # Create a distinct color map with one color per cluster # Assign gray to noise cluster colors = ['#0096FF'] + [plt.cm.tab20(i) for i in range(1, n_clusters)] cmap = ListedColormap(colors) #cmap = plt.cm.get_cmap('tab20', n_clusters) # Use tab20, tab10, or other qualitative colormaps # Plot plt.figure(figsize=(10, 7)) scatter = plt.scatter( X_umap[:, 0], X_umap[:, 1], c=cluster_labels, cmap=cmap, s=10 ) plt.title("UMAP + HDBSCAN Clustering of Entire Dataset") plt.xlabel("UMAP1") plt.ylabel("UMAP2") plt.grid(True) # Add colorbar with correct ticks cbar = plt.colorbar(scatter, ticks=unique_labels) cbar.set_label('Cluster ID') plt.show() print(df_clustered['HDBSCAN_cluster'].value_counts().sort_values(ascending=False)) Continue reading...
