This is my first time implementing a Machine Learning Algorithm in Python. I tried implementing K-Means using Python and Sklearn for this dataset.

from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

# Importing the dataset
data = pd.read_csv('dataset.csv')
print("Input Data and Shape")

# Getting the values and plotting it
f1 = data['Area'].values
f2 = data['perimeter'].values
f3 = data['Compactness'].values
f4 = data['length_kernel'].values
f5 = data['width_kernel'].values
f6 = data['asymmetry'].values
f7 = data['length_kernel_groove'].values

X = np.array(list(zip(f1,f2,f3,f4,f5,f6,f7)))
# Number of clusters
kmeans = KMeans(n_clusters=7)
kmeans = kmeans.fit(X)
# Getting the cluster labels
labels = kmeans.predict(X)
# Centroid values
centroids = kmeans.cluster_centers_

plt.scatter(X[:,0], X[:,1],cmap='rainbow')
plt.scatter(centroids[:,0], centroids[:1], color="black", marker='*')

The graph doesn't seem to plot the data correctly. How can I debug this issue?


  • $\begingroup$ What is the dimensionality (ie. shape) of the array X? $\endgroup$ – JahKnows Nov 15 '17 at 8:03

Well, there are some issues:

  • Dimension vs K: Before talking about visualization I would like to address some clustering concept. Your data is in 7 dimensions but it does not mean that you have 7 clusters! Be careful here. For instance I have two features of people let's say salary and number of years they have working experience. Here I have two features but does it mean that there necessary two categories inside the data? sure not!

  • Visualization: Your data is in 7 dimension which is not visualizable. So you decided to reduce this to two which is a correct approach but you did a wrong thing for this correct approach. You can not take the first two features to visualize 7 dimensions, you need to REDUCE it to two features using Dimensionality Reduction algorithms like PCA, NMF, etc. What you did is actually IGNORING 5 dimensions of the points which are extremely informative for placing them in a 7-dimensional space.


Everything is right. Just add a PCA to your code like this:

From sklearn.decomposition import PCA
Model = PCA(n_components=2)
X_new = Model.fit_transform(X)
... Use X_new instead of X for K-means procedure

Please note that I wrote this relying on my memory so better to check the documentation if I had a typo or smth. In case you have more question you can comment here.

Good Luck!


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.