SSE Calculation for a Clustering

By Debasis Das: (17-Feb-2021)

How to manually calculate the SSE for a Clustering.

Clustering such as KMeans has a inertia_ function that gives the total SSE for the clustering, however clustering such as DBScan lacks an inertia_ function and in this sample code we are going to see how we can derive the SSE number for a clustering

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA

A = [[10,10],

df = pd.DataFrame(A)
number_clusters = 2
pca = PCA(n_components=2)
x_pca = pca.fit_transform(df)
km = KMeans(
    n_clusters=number_clusters, init='random',
    n_init=10, max_iter=300, 
    tol=1e-04, random_state=0

kmeans_labels = km.labels_
print("The KMeans Labels are = ",kmeans_labels)
print("The Kmeans SSE using inertia_ function =", km.inertia_)

manual_SSE = 0
for i in range(number_clusters):
    cluster = x_pca[km.labels_ == i]
    if len(cluster) > 0:
            clusterMean = cluster.mean(axis = 0)
            manual_SSE += ((cluster - clusterMean) ** 2).sum()

print("The KMeans SSE using manual calculation = ",manual_SSE)  

# Clustering such as DBScan doesnot have a inertia function 
# and in case one needs to calculate the SSE for a DBScan clustering, 
# we can use the manual method of SSE Calculation

Output: for the given dataset

The KMeans Labels are =  [0 0 0 1 1 1 1]
The Kmeans SSE using inertia_ function = 19.04166666666666
The KMeans SSE using manual calculation =  19.041666666666664

As you can see the SSE calculated matches the SSE given by the inertia_ function.

You can use the same manual approach of using the DBScan Cluster and DBScan Labels to come up with the SSE for DBScan Clustering

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