Implementation of DBSCAN from Scratch

machine_learning/DBSCAN.py

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+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+
+class DBSCAN:
+    '''
+    Author : Gowtham Kamalasekar
+    LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
+
+    DBSCAN Algorithm :
+    Density-Based Spatial Clustering Of Applications With Noise
+    Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
+
+    Attributes:
+    -----------
+        minPts (int) : Minimum number of points needed to be within the radius to considered as core
+        radius (int) : The radius from a given core point where other core points can be considered as core
+        file (csv)   : CSV file location. Should contain x and y coordinate value for each point.
+
+        Example :
+        minPts = 4
+        radius = 1.9
+        file = 'data_dbscan.csv'
+
+    File Structure of CSV Data:
+    ---------------------------
+        _____
+        x | y
+        -----
+        3 | 7
+        4 | 6
+        5 | 5
+        6 | 4
+        7 | 3
+        -----
+
+    Functions:
+    ----------
+        __init__()       : Constructor that sets minPts, radius and file
+        perform_dbscan() : Invoked by constructor and calculates the core and noise points and returns a dictionary.
+        print_dbscan()   : Prints the core and noise points along with stating if the noise are border points or not.
+        plot_dbscan()    : Plots the points to show the core and noise point.
+
+    To create a object
+    ------------------
+    import DBSCAN
+    obj = DBSCAN.DBSCAN(minPts, radius, file)
+    obj.print_dbscan()
+    obj.plot_dbscan()
+    '''
+
+    def __init__(self, minPts, radius, file):
+        self.minPts = minPts
+        self.radius = radius
+        self.file = file
+        self.dict1 = self.perform_dbscan()
+
+    def perform_dbscan(self):
+        data = pd.read_csv(self.file)
+
+        minPts = self.minPts
+        e = self.radius
+
+        dict1 = {}
+        for i in range(len(data)):
+            for j in range(len(data)):
+                dist = math.sqrt(pow(data['x'][j] - data['x'][i],2) + pow(data['y'][j] - data['y'][i],2))
+                if dist < e:
+                    if i+1 in dict1:
+                        dict1[i+1].append(j+1)
+                    else:
+                        dict1[i+1] = [j+1,]
+
+        return dict1
+
+    def print_dbscan(self):
+        for i in self.dict1:
+            print(i," ",self.dict1[i], end=' ---> ')
+            if len(self.dict1[i]) >= self.minPts:
+                print("Core")
+            else:
+                for j in self.dict1:
+                    if i != j and len(self.dict1[j]) >= self.minPts and i in self.dict1[j]:
+                        print("Noise ---> Border")
+                        break
+                else:
+                    print("Noise")
+
+    def plot_dbscan(self):
+        data = pd.read_csv(self.file)
+        e = self.radius
+        for i in self.dict1:
+            if len(self.dict1[i]) >= self.minPts:
+                plt.scatter(data['x'][i-1], data['y'][i-1], color='red')
+                circle = plt.Circle((data['x'][i-1], data['y'][i-1]), e, color='blue', fill=False)
+                plt.gca().add_artist(circle)
+                plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
+            else:
+                plt.scatter(data['x'][i-1], data['y'][i-1], color='green')
+                plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
+
+        plt.xlabel('X')
+        plt.ylabel('Y')
+        plt.title('DBSCAN Clustering')
+
+        plt.legend(['Core','Noise'])
+        plt.show()