Clustering in Machine Learning

Clustering in Machine Learning
Clustering or cluster analysis is a machine learning technique, which groups the

unlabelled dataset. It can be defined as "A way of grouping the data points into
different clusters, consisting of similar data points. The objects with the
possible similarities remain in a group that has less or no similarities with
another group."
It is an unsupervised learning method, hence no supervision is provided to the

algorithm, and it deals with the unlabeled dataset.
The clustering technique is commonly used for statistical data analysis.
The clustering technique can be widely used in various tasks. Some most common
uses of this technique are:
o Market Segmentation
o Statistical data analysis
o Social network analysis
o Image segmentation
o Anomaly detection, etc.
Apart from these general usages, it is used by the Amazon in its recommendation
system to provide the recommendations as per the past search of
products. Netflix also uses this technique to recommend the movies and web-series
to its users as per the watch history.
The below diagram explains the working of the clustering algorithm. We can see the
different fruits are divided into several groups with similar properties.
Types of Clustering Methods
The clustering methods are broadly divided into Hard clustering (datapoint belongs
to only one group) and Soft Clustering (data points can belong to another group
also). But there are also other various approaches of Clustering exist. Below are the
main clustering methods used in Machine learning:
1. Partitioning Clustering
2. Density-Based Clustering
3. Distribution Model-Based Clustering
4. Hierarchical Clustering
5. Fuzzy Clustering
Partitioning Clustering
It is a type of clustering that divides the data into non-hierarchical groups. It is also
known as the centroid-based method. The most common example of partitioning
clustering is the K-Means Clustering algorithm.
In this type, the dataset is divided into a set of k groups, where K is used to define
the number of pre-defined groups. The cluster center is created in such a way that
the distance between the data points of one cluster is minimum as compared to
another cluster centroid.
Density-Based Clustering
The density-based clustering method connects the highly-dense areas into clusters,
and the arbitrarily shaped distributions are formed as long as the dense region can
be connected. This algorithm does it by identifying different clusters in the dataset
and connects the areas of high densities into clusters. The dense areas in data
space are divided from each other by sparser areas.
These algorithms can face difficulty in clustering the data points if the dataset has
varying densities and high dimensions.
Distribution Model-Based Clustering
In the distribution model-based clustering method, the data is divided based on the
probability of how a dataset belongs to a particular distribution. The grouping is done
by assuming some distributions commonly Gaussian Distribution.
The example of this type is the Expectation-Maximization Clustering

algorithm that uses Gaussian Mixture Models (GMM).
Hierarchical Clustering
Hierarchical clustering can be used as an alternative for the partitioned clustering as
there is no requirement of pre-specifying the number of clusters to be created. In this
technique, the dataset is divided into clusters to create a tree-like structure, which is
also called a dendrogram. The observations or any number of clusters can be
selected by cutting the tree at the correct level. The most common example of this
method is the Agglomerative Hierarchical algorithm.
Fuzzy Clustering
Fuzzy clustering is a type of soft method in which a data object may belong to more
than one group or cluster. Each dataset has a set of membership coefficients, which
depend on the degree of membership to be in a cluster. Fuzzy C-means
algorithm is the example of this type of clustering; it is sometimes also known as the
Fuzzy k-means algorithm.
Clustering Algorithms
The Clustering algorithms can be divided based on their models that are explained
above. There are different types of clustering algorithms published, but only a few
are commonly used. The clustering algorithm is based on the kind of data that we
are using. Such as, some algorithms need to guess the number of clusters in the
given dataset, whereas some are required to find the minimum distance between the
observation of the dataset.
Here we are discussing mainly popular Clustering algorithms that are widely used in
machine learning:
1. K-Means algorithm: The k-means algorithm is one of the most popular

clustering algorithms. It classifies the dataset by dividing the samples into
different clusters of equal variances. The number of clusters must be specified
in this algorithm. It is fast with fewer computations required, with the linear
complexity of O(n).
2. Mean-shift algorithm: Mean-shift algorithm tries to find the dense areas in
the smooth density of data points. It is an example of a centroid-based model,
that works on updating the candidates for centroid to be the center of the
points within a given region.
3. DBSCAN Algorithm: It stands for Density-Based Spatial Clustering of
Applications with Noise. It is an example of a density-based model similar to
the mean-shift, but with some remarkable advantages. In this algorithm, the
areas of high density are separated by the areas of low density. Because of
this, the clusters can be found in any arbitrary shape.
4. Expectation-Maximization Clustering using GMM: This algorithm can be
used as an alternative for the k-means algorithm or for those cases where K-
means can be failed. In GMM, it is assumed that the data points are Gaussian
distributed.
5. Agglomerative Hierarchical algorithm: The Agglomerative hierarchical
algorithm performs the bottom-up hierarchical clustering. In this, each data
point is treated as a single cluster at the outset and then successively
merged. The cluster hierarchy can be represented as a tree-structure.
6. Affinity Propagation: It is different from other clustering algorithms as it does
not require to specify the number of clusters. In this, each data point sends a
message between the pair of data points until convergence. It has O(N2T)
time complexity, which is the main drawback of this algorithm.
Applications of Clustering
Below are some commonly known applications of clustering technique in Machine
Learning:
o In Identification of Cancer Cells: The clustering algorithms are widely used

for the identification of cancerous cells. It divides the cancerous and non-
cancerous data sets into different groups.
o In Search Engines: Search engines also work on the clustering technique.
The search result appears based on the closest object to the search query. It
does it by grouping similar data objects in one group that is far from the other
dissimilar objects. The accurate result of a query depends on the quality of the
clustering algorithm used.
o Customer Segmentation: It is used in market research to segment the
customers based on their choice and preferences.
o In Biology: It is used in the biology stream to classify different species of
plants and animals using the image recognition technique.
o In Land Use: The clustering technique is used in identifying the area of
similar lands use in the GIS database. This can be very useful to find that for
what purpose the particular land should be used, that means for which
purpose it is more suitable.

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Clustering in Machine Learning

Clustering or cluster analysis is a machine learning technique, which groups the

It is an unsupervised learning method, hence no supervision is provided to the

The clustering technique is commonly used for statistical data analysis.

The example of this type is the Expectation-Maximization Clustering

1. K-Means algorithm: The k-means algorithm is one of the most popular

o In Identification of Cancer Cells: The clustering algorithms are widely used

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