Implementing DBSCAN algorithm using Sklearn
Prerequisites: DBSCAN Algorithm
Density Based Spatial Clustering of Applications with Noise(DBCSAN) is a clustering algorithm which was proposed in 1996. In 2014, the algorithm was awarded the ‘Test of Time’ award at the leading Data Mining conference, KDD.
Dataset – Credit Card.
Step 1: Importing the required libraries
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import normalize from sklearn.decomposition import PCA |
Step 2: Loading the data
X = pd.read_csv('..input_path/CC_GENERAL.csv') # Dropping the CUST_ID column from the data X = X.drop('CUST_ID', axis = 1) # Handling the missing values X.fillna(method ='ffill', inplace = True) print(X.head()) |
Step 3: Preprocessing the data
# Scaling the data to bring all the attributes to a comparable level scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Normalizing the data so that # the data approximately follows a Gaussian distribution X_normalized = normalize(X_scaled) # Converting the numpy array into a pandas DataFrame X_normalized = pd.DataFrame(X_normalized) |
Step 4: Reducing the dimensionality of the data to make it visualizable
pca = PCA(n_components = 2) X_principal = pca.fit_transform(X_normalized) X_principal = pd.DataFrame(X_principal) X_principal.columns = ['P1', 'P2'] print(X_principal.head()) |
Step 5: Building the clustering model
# Numpy array of all the cluster labels assigned to each data point db_default = DBSCAN(eps = 0.0375, min_samples = 3).fit(X_principal) labels = db_default.labels_ |
Step 6: Visualizing the clustering
# Building the label to colour mapping colours = {} colours[0] = 'r'colours[1] = 'g'colours[2] = 'b'colours[-1] = 'k' # Building the colour vector for each data point cvec = [colours[label] for label in labels] # For the construction of the legend of the plot r = plt.scatter(X_principal['P1'], X_principal['P2'], color ='r'); g = plt.scatter(X_principal['P1'], X_principal['P2'], color ='g'); b = plt.scatter(X_principal['P1'], X_principal['P2'], color ='b'); k = plt.scatter(X_principal['P1'], X_principal['P2'], color ='k'); # Plotting P1 on the X-Axis and P2 on the Y-Axis # according to the colour vector defined plt.figure(figsize =(9, 9)) plt.scatter(X_principal['P1'], X_principal['P2'], c = cvec) # Building the legend plt.legend((r, g, b, k), ('Label 0', 'Label 1', 'Label 2', 'Label -1')) plt.show() |
Step 7: Tuning the parameters of the model
db = DBSCAN(eps = 0.0375, min_samples = 50).fit(X_principal) labels1 = db.labels_ |
Step 8: Visualizing the changes
colours1 = {} colours1[0] = 'r'colours1[1] = 'g'colours1[2] = 'b'colours1[3] = 'c'colours1[4] = 'y'colours1[5] = 'm'colours1[-1] = 'k' cvec = [colours1[label] for label in labels] colors = ['r', 'g', 'b', 'c', 'y', 'm', 'k' ] r = plt.scatter( X_principal['P1'], X_principal['P2'], marker ='o', color = colors[0]) g = plt.scatter( X_principal['P1'], X_principal['P2'], marker ='o', color = colors[1]) b = plt.scatter( X_principal['P1'], X_principal['P2'], marker ='o', color = colors[2]) c = plt.scatter( X_principal['P1'], X_principal['P2'], marker ='o', color = colors[3]) y = plt.scatter( X_principal['P1'], X_principal['P2'], marker ='o', color = colors[4]) m = plt.scatter( X_principal['P1'], X_principal['P2'], marker ='o', color = colors[5]) k = plt.scatter( X_principal['P1'], X_principal['P2'], marker ='o', color = colors[6]) plt.figure(figsize =(9, 9)) plt.scatter(X_principal['P1'], X_principal['P2'], c = cvec) plt.legend((r, g, b, c, y, m, k), ('Label 0', 'Label 1', 'Label 2', 'Label 3 'Label 4', 'Label 5', 'Label -1'), scatterpoints = 1, loc ='upper left', ncol = 3, fontsize = 8) plt.show() |
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