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Author: kamlesh0928

DBSCAN Clustering/dbscan_clustering_implementation_from_scratch.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_moons
+from sklearn.preprocessing import StandardScaler
+
+# Generate the dataset
+X, _ = make_moons(n_samples = 1000, noise = 0.1, random_state = 42)
+
+X = StandardScaler().fit_transform(X)
+
+class DBSCAN:
+
+    def __init__(self, eps, min_points):
+        self.eps = eps
+        self.min_points = min_points
+        self.labels = None
+
+
+    def fit(self, X):
+        self.labels = np.full(len(X), -1)   # Initialize all labels to -1 (noise)
+        cluster_id = 0
+
+        for point_idx in range(len(X)):
+            if(self.labels[point_idx] != -1):   # Already visited
+                continue
+
+            neighbors = self.region_query(X, point_idx)
+
+            if(len(neighbors) < self.min_points):
+                self.labels[point_idx] = -1     # Mark as noise
+            else:
+                cluster_id += 1
+                self.labels[point_idx] = cluster_id     # Start a new cluster
+                self.expand_cluster(X, neighbors, cluster_id)
+
+        return self.labels
+
+
+    def euclidean_distance(self, point1, point2):
+        return np.sqrt(np.sum((point1 - point2) ** 2))
+
+
+    def region_query(self, X, point_idx):
+
+        neighbors = []
+
+        for idx in range(len(X)):
+            if(self.euclidean_distance(X[point_idx], X[idx]) < self.eps):
+                neighbors.append(idx)
+
+        return neighbors
+
+
+    def expand_cluster(self, X, neighbors, cluster_id):
+
+        for neighbor_idx in neighbors:
+            if(self.labels[neighbor_idx] == -1):    # Previously marked as noise
+                self.labels[neighbor_idx] = cluster_id
+
+            elif(self.labels[neighbor_idx] != 0):   # Already visited
+                continue
+
+            self.labels[neighbor_idx] = cluster_id
+
+            # Get new neighbors and expand if needed
+            new_neighbors = self.region_query(X, neighbor_idx)
+
+            if(len(new_neighbors) >= self.min_points):
+                self.expand_cluster(X, new_neighbors, cluster_id)
+
+
+dbscan = DBSCAN(eps=0.2, min_points=5)
+labels = dbscan.fit(X)
+
+
+def plot_clusters(X, labels):
+
+    plt.figure(figsize = (10, 6))
+
+    for label in set(labels):
+        color = "black" if label == -1 else plt.cm.rainbow(label / len(set(labels)))
+        plt.scatter(X[labels == label, 0], X[labels == label, 1], color=color, label=f"Cluster {label}" if label != -1 else "Noise")
+
+    plt.title("DBSCAN Clustering")
+    plt.xlabel("Feature 1")
+    plt.ylabel("Feature 2")
+    plt.legend()
+    plt.show()
+
+plot_clusters(X, labels)

DBSCAN Clustering/dbscan_clustering_sklearn.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_moons
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import DBSCAN
+
+# Generate the dataset
+X, _ = make_moons(n_samples = 1000, noise = 0.1, random_state = 42)
+
+X = StandardScaler().fit_transform(X)
+
+dbscan = DBSCAN(eps = 0.2, min_samples = 5)
+labels = dbscan.fit_predict(X)
+
+
+def plot_clusters(X, labels):
+
+    plt.figure(figsize = (10, 6))
+
+    for label in set(labels):
+        color = "black" if label == -1 else plt.cm.rainbow(label / len(set(labels)))
+        plt.scatter(X[labels == label, 0], X[labels == label, 1], color=color, label=f"Cluster {label}" if label != -1 else "Noise")
+
+    plt.title("DBSCAN Clustering")
+    plt.xlabel("Feature 1")
+    plt.ylabel("Feature 2")
+    plt.legend()
+    plt.show()
+
+plot_clusters(X, labels)