Python Mean Shift Clustering Definition and Example Code
In the area of data science, the term “mean shift clustering in python” refers to a classification of “unsupervised learning algorithm” that is concerned with the process of “grouping data points in a sample space.” Unsupervised learning refers to a category of machine learning algorithms that focuses on recognising patterns in data that does not have a label associated to itself. This kind of learning is used to develop autonomous decisionmaking systems. In addition, the name of the algorithm gives the impression that it can recognise patterns even in the absence of human intervention. The essence of the method is to keep repeatedly assigning data points by shifting the points towards the mode. This is done in order to complete the process. The mode is the section of the discrete data distribution where the density of the data points in the area is at its highest, and it generally corresponds to the maximum point in the distribution. This algorithm is a nonparametric approach that is used for analysis and identification of the mode or maximum. It is sometimes referred to as a modeseeking algorithm because of its frequent use in these activities.
Syntax
Python code to import the meanshift library:
from sklearn.cluster import MeanShift
Taking into account the estimation
bandwidth:
from sklearn.cluster import estimate_bandwidth
This library provides assistance in calculating the bandwidth that is necessary for the operation of a Radial Basis Kernel. This is determined by calculating the average distance between each pair of points that are included in the cluster. The distances are determined pairwise.
Creating an instance of the object belonging to the meanshift module, which
imported:
<variable name> = MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=None, max_iter=300)
A breakdown of the various settings and parameters.
is:

bandwidth:
Calculated for RBF kernel as explained in the above syntax. 
seeds:
This parameter is to initialize the kernels. In case this is not set, by default seeds are calculated using clustering.get_bin_seeds and the bandwidth is used as the grid size. 
bin_seeding:
This is a Boolean parameter, where when true, the initial kernel location is the location of discretized version of binned points and coarseness depends on the bandwidth. 
min_bin_freq:
This parameter is set to accept only those bins that have a minimum seed as provided in this parameter 
cluster_all:
This is a Boolean parameter as well and when true it makes sure that all the data points are clustered. In case of any outlier (ones with no clusters), they are assigned with the nearest cluster. On the other hand, when the argument is set to false the outliers are assigned 1 as a label 
n_jobs:
This parameter is to denote the umber of jobs that is to be used for computation. 
max_iter:
This denotes the maximum iterations that will take place before the job terminates until and unless the job hasnâ€™t converged till then.
Adjust the value of the object variable in the meanshift module, which
declared:
<variable name>.fit(<data set>)
The mean shift clustering algorithm in python: how does it work?
Before we go headfirst into the algorithm’s inner workings, let’s begin by getting a sense of what the algorithm is trying to accomplish. The concept of mean shift clustering is conceptually similar to that of hierarchical clustering, in which each data point is originally allocated to one of its own distinct clusters at the beginning of the clustering process. After that, with a given parameter, the cluster will begin to group a new data point at each iteration, and it will continue to do so until the group no longer includes any additional data points. As a result of this, we now have the clusters.
To go a little more into the topic at hand, the meanshift technique is based on the idea of Kernel Density Estimate, often known as KDE for its acronym. Estimating the underlying distribution of a dataset may be done with the help of the KDE approach. With this approach, a kernel is superimposed on each individual data point. Those who aren’t acquainted with the term “kernel” should know that it refers to a straightforward weighting function that can be applied to the lines of any kind of image processing in order to emphasise or suppress a pixel value (depending on the use case). When all of these kernels are combined together in a hierarchical method, the probability that results is eventually generated is:
distribution.
When there are no higher peaks near the 30range, we don’t sum up the local maxima peak with the other maxima, and as a result, we get the 2 maxima peaks in the above graph. This is because the initial distribution of all the histograms in the above graph is a distribution. When the distributions of the neighbours are added, to reach to a higher peak than where it was previously, until we see that there are no more peaks.
In the same manner, when performing a mean shift operation on a data distribution, each data point is assigned its own unique distribution. From this point on, the chronological phases take place in the sequence necessary to reach the end clusters.
1. We start with a single piece of data and proceed with the following stages. These procedures are carried out on each and every data point that is present in the space. 2. Using a bandwidth, which is a parameter that we covered in the section on syntax, we construct an area around the data point, and then we collect all of the points that fall inside that region. 3. Whenever we obtain a significant amount of additional data points, we calculate the mean of all of those data points and then designate the mean of those data points as the new mean (This is where we do the mean shift). 4. The procedure described above is performed many times until the cluster no longer has any further data points inside it. 5. When the procedure has been carried out for all of the data points, we will eventually arrive at the clustered data points. This will be the same as if all of the points in the sample space had reached their respective local maximas.
Examples
Let’s have a look at a realworld use of Mean Shift Clustering in Python.
Clustering based on mean shifts using the sklearn module: Code:
import numpy as np
import pandas as pd
from sklearn.cluster import MeanShift,estimate_bandwidth
from sklearn.datasets import make_blobs
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
clusters = [[27, 72, 91], [36, 90, 99], [9, 81, 99]]
Output:
#Making the random data set
X, _ = make_blobs(n_samples = 180, centers = clusters,
cluster_std = 2.79)
# Estimating the bandwith (or the radius which needs to be taken as part of step 2)
bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)
meanshift = MeanShift(bandwidth=bandwidth)
meanshift.fit(X)
labels = meanshift.labels_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
print('Estimated number of clusters: ' + str(n_clusters_))
y_pred = meanshift.predict(X)
plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap="viridis")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
Here, we see that the random data set that was created has the potential to produce three local minima, and we also see that the data points are precisely clustered in all three of these spots!