Ordering Points To Identify Clustering Structure(OPTICS) is a clustering algorithm that is an improvement of the DBSCAN algorithm. OPTICS can find clusters of varying density as well, which DBSCAN was not able to do due to fixed “eps”. More information about these algorithms can be found here.
You can learn more about the OPTICS algorithm in the below video.
The below code snippet will help to create clusters using OPTICS.
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# Sample code to create OPTICS Clustering in Python # Creating the sample data for clustering from sklearn.datasets import make_blobs import matplotlib.pyplot as plt import numpy as np import pandas as pd # create sample data for clustering SampleData = make_blobs(n_samples=100, n_features=2, centers=2, cluster_std=1.5, random_state=40) #create np array for data points X = SampleData[0] y = SampleData[1] # Creating a Data Frame to represent the data with labels ClusterData=pd.DataFrame(list(zip(X[:,0],X[:,1],y)), columns=['X1','X2','ClusterID']) print(ClusterData.head()) # create scatter plot to visualize the data %matplotlib inline plt.scatter(ClusterData['X1'], ClusterData['X2'], c=ClusterData['ClusterID']) ################################################################################## # This function is not present in python version 3.6 # Other option is pyclustering.cluster.optics but its not neat from sklearn.cluster import OPTICS op = OPTICS(min_samples=40, xi=0.02, min_cluster_size=0.1) # Generating cluster id for each row using DBSCAN algorithm ClusterData['PredictedClusterID']=op.fit_predict(X) print(ClusterData.head()) # Plotting the predicted clusters plt.scatter(ClusterData['X1'], ClusterData['X2'], c=ClusterData['PredictedClusterID']) |
Sample Output:
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To the Point Explanation! Helped a Lot, Thank You so much Farukh Sir, your teaching is Awesome
Thank you for the kind words Sagar!