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 npimport pandas as pdimport matplotlib.pyplot as plt from sklearn.cluster import DBSCANfrom sklearn.preprocessing import StandardScalerfrom sklearn.preprocessing import normalizefrom 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 dataX = X.drop('CUST_ID', axis = 1) # Handling the missing valuesX.fillna(method ='ffill', inplace = True) print(X.head()) |
Step 3: Preprocessing the data
# Scaling the data to bring all the attributes to a comparable levelscaler = StandardScaler()X_scaled = scaler.fit_transform(X) # Normalizing the data so that # the data approximately follows a Gaussian distributionX_normalized = normalize(X_scaled) # Converting the numpy array into a pandas DataFrameX_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 pointdb_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 mappingcolours = {}colours[0] = 'r'colours[1] = 'g'colours[2] = 'b'colours[-1] = 'k' # Building the colour vector for each data pointcvec = [colours[label] for label in labels] # For the construction of the legend of the plotr = 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 definedplt.figure(figsize =(9, 9))plt.scatter(X_principal['P1'], X_principal['P2'], c = cvec) # Building the legendplt.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() |
