Clustering (k) Python is one of the common machine learning approaches used to find data item clusters. There are many clustering algorithms, but k means is the oldest and most popular. So many data scientists and programmers utilize k-means clustering in Python. Keep reading to learn how to use k-means clustering in Python. This blog has covered all the important basics about K-means clustering, including an example of how it works.

What is clustering and its use?

Clustering is a set of strategies for grouping or clustering data. Clusters are groups of data objects that are similar to each other. In practice, clustering helps distinguish two data qualities:






Meaning Clusters


Useful Groups


It broadens the horizons.


It’s a step in the data pipeline.


For example, in the medical field, researchers employed clustering to study gene expression. This approach identifies patients who do not respond to a certain medicinal treatment. For example, several organizations employ clustering to segment their clients. By grouping customers who make similar purchases, firms can simply construct targeted advertising campaigns.


Other uses of k means clustering Python include social network analysis and document clustering. Thus, these applications are applicable in practically any business. So clustering becomes a valuable skill for experts working with various data.


What are the clustering methods?


Choosing the appropriate clustering algorithm for a given dataset is always difficult. Some essential parameters always influence the decision, such as dataset features, cluster characteristics, data object count, and outlier count. Listed below are the three most popular clustering algorithms:


Clustering by partitions


2-D density clustering


  1. Clustering




Splitting Clusters


It separates the non-overlapping data objects. Or, no object can belong to more than one cluster, and each cluster has at least one object.


In this technique, the user declares the number of clusters (k). Several partitional clustering algorithms work iteratively to assign a data object dataset to a k cluster. Partitional clustering algorithms like k-medoids and k-means


Clustering by Density


It analyzes cluster assignments based on data point density. Low-density regions classify high-density data points.


It, like other clustering categories, does not require cluster numbers. Distant factors are always a variable threshold (the threshold can analyze how close the points can be considered to a clustering member). Density-based cluster algorithms include OPTICS and Noise.




It also sorts the clustering assignments into a hierarchy. It can take two approaches:


Divisive clustering: The top-down approach starts with a single cluster and divides the less similar clusters until only one data point remains.


Bottom-up clustering merges two similar points until they do not form a single cluster.


This method creates a dendrogram, which is a tree-like hierarchy of points. Like in partitional clustering, the user chooses the cluster number (k).


Which k means clustering Python method is best?




It has been observed that a conventional k means need just a few steps to execute. That starts with selecting k centroids, where the value of k = the number of clusters that you have selected. Centroids are the specialized data points that represent the cluster’s center.


The k means clustering Python algorithm’s main components always work in a two-step process known as expectation-maximization. Initially, the expectation step is assigned by every data point to a specific centroid that is nearer to it. Then, with the help of the maximization step, the computation of the nearer points can be done. This algorithm works as:




Specifying the number of k clusters to assign the value.




Initializing the k centroid randomly.




Repeat the process.




Expectation: Assigning every point to its nearer centroid.




Maximization: Computing the mean (or new centroid) of every cluster.




Till the position of the centroid does not change.


The cluster assignments’ quality can be determined by computing the SSE (Squared Error) after matching the previous iteration’s assignment or using centroid converge. SSE measures the error that is trying to minimize the k means value. The below-mentioned figure can display SSE and centroids that update the first five iterations in the different runs.


In this figure, you can check the initialization of the particular centroid. Moreover, it highlights the objective of SSE that use to measure clustering performance. Once the several clusters are chosen and initialized the centroids, the expectation-maximization step will repeat till the position of the centroid converges and unchanged.


An example of k means clustering Python


Create the DataFrame for the 2D dataset


To start with the example, let’s take an example of the following 2D dataset:


x y


22 78


35 51


20 52


25 76


32 57


31 72


20 71


34 55


32 67


65 73


52 49


55 30


42 38


50 45


55 51


57 34


50 33


63 56


45 57


47 48


46 23


33 18


31 12


43 10


45 18


36 3


41 27


51 6


44 5


You can write the data for k means clustering Python with the help of Pandas DataFrame.


from pandas import DataFrame


Data = {‘x’: [22,35,20,25,32,31,20,34,32,65,52,55,42,50,55,57,50,63,45,47,46,33,31,43,45,36,41,51,44], ‘y’: [78,51,52,76,57,72,71,55,67,73,49,30,38,45,51,34,33,56,57,48,23,18,12,10,18,3,27,6,5]


} df = DataFrame(Data,columns=[‘x’,’y’])


print (df) (df)




x y


22 78


1 35 51


2 20 52


3 25 76


4 32 57


5 31 72


6 20 71


7 34 55


8 32 67


9 65 73


10 52 49


11 55 30


12 42 38


13 50 45


14 55 51


15 57 34


16 50 33


17 63 56


18 45 57


19 47 48


20 46 23


21 33 18


22 31 12


23 43 10


24 45 18


25 36 3


26 41 27


27 51 6


28 44 5


K means clustering Python (3 clusters) (3 clusters)


Once you are done with creating the DataFrame depend on the above set of data, you are required to import some of the additional Python modules:




The below-mentioned code is used to declare the number of clusters. To understand it, let’s take an example of 3 clusters:


KMeans(n clusters=3).




Import DataFrameimport Matplotlib from pandas.


import KMeans from sklearn.cluster


« x »: []; « y »: [


df = DataFrame(Data,x,y) KMeans(n clusters=3) kmeans.cluster centers print (centroids)


S=30, alpha=0.6, df[‘x’], df[‘y’], c= kmeans.labels .astype(float),


[:, 0], [:, 1], c=’red’, 30)


Assume that you have three clusters at three different centroids.




The red center of each cluster describes the mean of the observations within that cluster. Also, the observations are significantly closer to the cluster’s center than the other clusters’ centers.






Clustering Python is one of the unsupervised machine learning ideas. Its algorithm also finds groupings in unlabeled data. This blog discussed clustering algorithms and generated a DataFrame for a 2D dataset. We also showed how to find the centroid of three clusters. If you have any questions about clustering, you may contact us and ask our specialists by leaving a comment below. We will offer you with a great answer. So keep practicing and learning. We offer cheap python programming help.