Important Clustering Algorithms in Machine Learning

Introduction

Clustering is a Machine Learning method. It implicates the grouping of data points. It is an unsupervised machine learning task. In which, we draw references from datasets consisting of input data without labelled responses. With a clustering algorithm, we give the algorithm a lot of input data with no labels and let it find any groupings in the data it can.

We can use a clustering algorithm to categorize each data point into a specific group. Those are given a set of data points. Data points that are in the same group should have similar properties. Though data points in different groups should have very unlike properties and features.

Description

In this post, we will first be acquainted with what is cluster before going to discuss in detail the clustering algorithms. A cluster is a group of data points that are alike to each other founded on their relation to nearby data points. Clustering is often used for things similar to feature engineering or design discovery.

Clustering Techniques

Density-Based

  • These methods reflect the clusters as the dense region having some similarities and differences from the lower dense region of the space.
  • These methods have good correctness and aptitude to merge two clusters.

Hierarchical Based

  • The clusters shaped in this method forms a tree-type structure built on the hierarchy.
  • New clusters are made using the before formed one.
  • It is distributed into two groups:
  • Agglomerative (bottom-up method)
  • Divisive (top-down method)

Partitioning Methods  

  • These methods partition the substances into k clusters.
  • Each partition forms one cluster.
  • This method is used to enhance an objective criterion resemblance function.

Grid-based Methods

  • The data space is expressed into a finite number of cells that form a grid-like structure in this method.
  • All the clustering operations completed on these grids are fast and free of the number of data objects.

Important Clustering algorithms

Now, we’re going to look at popular clustering algorithms that every data scientist should need to know.

K-means clustering algorithm

  • It is the most generally used clustering algorithm.
  • It’s the simplest unsupervised learning algorithm.
  • This algorithm tries to reduce the change of data points within a cluster.
  • It’s similar to how utmost people are familiarized with unsupervised machine learning.
  • K-means is best used on reduced data sets as it repeats over all of the data points.
  • That means if there is a big amount of them in the data set it’ll take more time to classify data points.
  • For the time being this is how k-means clusters data points, it doesn’t scale well.

Application

from numpy import unique
from numpy import where
from matplotlib import pyplot
from sklearn.datasets import make_classification
from sklearn.cluster import KMeans
# initialize the data set we'll work with
training_data, _ = make_classification(
   n_samples=1000,
   n_features=2,
   n_informative=2,
   n_redundant=0,
   n_clusters_per_class=1,
   random_state=4
)
# define the model
kmeans_model = KMeans(n_clusters=2)
# assign each data point to a cluster
dbscan_result = dbscan_model.fit_predict(training_data)
# get all of the unique clusters
dbscan_clusters = unique(dbscan_result)
# plot the DBSCAN clusters
for dbscan_cluster in dbscan_clusters:
   # get data points that fall in this cluster
   index = where(dbscan_result == dbscan_clusters)
   # make the plot
   pyplot.scatter(training_data[index, 0], training_data[index, 1])
# show the DBSCAN plot
pyplot.show()

Mean-Shift clustering algorithm

  • This algorithm is useful for handling images and computer vision processing.
  • It is like the BIRCH algorithm as it also finds clusters without an early number of clusters being set.
  • This is a hierarchical clustering algorithm.
  • It doesn’t scale well when operational with large data sets.
  • It works by iterating over all of the data points and moves them near the mode.
  • The mode in this setting is the high-density area of data points in an area.
  • We might hear this algorithm mentioned as the mode-seeking algorithm.

Application

from numpy import unique
from numpy import where
from matplotlib import pyplot
from sklearn.datasets import make_classification
from sklearn.cluster import MeanShift
# initialize the data set we'll work with
training_data, _ = make_classification(
   n_samples=1000,
   n_features=2,
   n_informative=2,
   n_redundant=0,
   n_clusters_per_class=1,
   random_state=4
)
# define the model
mean_model = MeanShift()
# assign each data point to a cluster
mean_result = mean_model.fit_predict(training_data)
# get all of the unique clusters
mean_clusters = unique(mean_result)
# plot Mean-Shift the clusters
for mean_cluster in mean_clusters:
   # get data points that fall in this cluster
   index = where(mean_result == mean_cluster)
   # make the plot
   pyplot.scatter(training_data[index, 0], training_data[index, 1])
# show the Mean-Shift plot
pyplot.show()

BIRCH algorithm

  • This algorithm works well on big data sets than the k-means algorithm.
  • It disrupts the data into slight summaries.
  • Those are clustered as an alternative to the original data points.
  • The outlines clench as much distribution information about the data points as possible.
  • This algorithm is normally used with a new clustering algorithm.
  • The other clustering methods may be used on the outlines made by BIRCH.
  • The BIRCH algorithm only works on numeric data values.
  • We can’t use this for categorical values unless we do some data transformations.

Application

from numpy import unique
from numpy import where
from matplotlib import pyplot
from sklearn.datasets import make_classification
from sklearn.cluster import Birch
# initialize the data set we'll work with
training_data, _ = make_classification(
   n_samples=1000,
   n_features=2,
   n_informative=2,
   n_redundant=0,
   n_clusters_per_class=1,
   random_state=4
)
# define the model
birch_model = Birch(threshold=0.03, n_clusters=2)
# train the model
birch_model.fit(training_data)
# assign each data point to a cluster
birch_result = birch_model.predict(training_data)
# get all of the unique clusters
birch_clusters = unique(birch_result)
# plot the BIRCH clusters
for birch_cluster in birch_clusters:
   # get data points that fall in this cluster
   index = where(birch_result == birch_clusters)
   # make the plot
   pyplot.scatter(training_data[index, 0], training_data[index, 1])
# show the BIRCH plot
pyplot.show()

Gaussian Mixture Model algorithm

  • This algorithm resolves the one problem that faces with k-means.
  • That is the data needs to follow a circular format in K-means.
  • The k-means doesn’t cluster non-circular data correctly.
  • The Gaussian mixture models fix this issue.
  • We don’t need circular-shaped data for it to work well.
  • The Gaussian mixture model uses many Gaussian distributions to fit arbitrarily shaped data.
  • There are some single Gaussian models, which act as hidden layers in this hybrid model.
  • Therefore, the model calculates the probability.
  • That a data point fits a specific Gaussian distribution.
  • That’s the cluster it will decrease under.

Application

from numpy import unique
from numpy import where
from matplotlib import pyplot
from sklearn.datasets import make_classification
from sklearn.mixture import GaussianMixture
# initialize the data set we'll work with
training_data, _ = make_classification(
   n_samples=1000,
   n_features=2,
   n_informative=2,
   n_redundant=0,
   n_clusters_per_class=1,
   random_state=4
)
# define the model
gaussian_model = GaussianMixture(n_components=2)
# train the model
gaussian_model.fit(training_data)
# assign each data point to a cluster
gaussian_result = gaussian_model.predict(training_data)
# get all of the unique clusters
gaussian_clusters = unique(gaussian_result)
# plot Gaussian Mixture the clusters
for gaussian_cluster in gaussian_clusters:
   # get data points that fall in this cluster
   index = where(gaussian_result == gaussian_clusters)
   # make the plot
   pyplot.scatter(training_data[index, 0], training_data[index, 1])
# show the Gaussian Mixture plot
pyplot.show()

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