Learn Clustering, its Methods, and Applications

Learn Clustering, its Methods, and Applications

Authored By admin

January 5, 2022

Introduction to clustering

Clustering is a method of machine learning that involves unsupervised learning. An unsupervised learning method consists of drawing references from datasets containing input data sans labeled responses. A multivariate data set can be examined using this exploratory technique.

Clustering of data sets is the process of dividing them into a certain number of groups or clusters where each group of data points has similar characteristics. A cluster is simply a group of data points grouped such that their distance from one another is as small as possible.

Types of Clustering

As a general rule, clustering can be broken down into two major groups viz. Hard Clustering and Soft Clustering. One data point that can be associated with only one cluster is hard clustering. In soft clustering, however, the result estimates the likelihood that each data point is part of each of the pre-defined clusters.

Major Clustering Methods

Density-Based Clustering: A clustering model searches for areas with varying densities of data points in the data space. Based on differences between densities in the data space, the algorithm isolates different regions of density.

Hierarchical Clustering: As the name implies, hierarchical clustering is an algorithm that builds clusters in a hierarchy. The algorithm assigns each data point to its own cluster and then merges two closer clusters together. In the end, this algorithm ends when the number of clusters is only one. Essentially, it consists of two categories:

  1.  Bottom-Up or agglomerative approach
  2. Top-Down Approach or Divisive

Distribution-based Clustering: It is a clustering model where we fit the data according to the probabilities of belonging to the same distribution. There are two kinds of grouping done: normal grouping and gaussian grouping. The Gaussian distribution is more prominent where we have a set number of distributions, and the data is fitted into those distributions to maximize the data distribution.

Clustering based on distributions produces models that assume concisely defined mathematical models underlie the distributions, an extremely strong assumption in some cases.

Centroid-based clustering: In this algorithm, the clusters are formed based on how close the points to the cluster’s centroid are. Cluster centers are formed so that the data points are spaced apart and at a minimum distance from each other.

Applications of Clustering

  • Data clustering plays a foundational role in exploratory data analysis (EDA), allowing for the initial discovery of patterns and features in data.
  • In search engine algorithms, clustering enables similar objects to be displayed together and dissimilar ones to be ignored.
  • Insurance industries often use clustering to detect fraudulent transactions and detect anomalies.
  • Biologists use cluster analysis to classify organisms genetically and taxonomically to understand their evolution and determine how they live.
  • Employers can segment resumes based on skills, experiences, strengths, types of projects, expertise, etc., enabling them to connect with the right job-seekers.
  • Clustering based on K-Means is an excellent way to identify spam. It looks at all sections of an email (header, sender, and content) to make this work. A group of data is then created. Spam can be identified by classifying these groups.


Although clustering is simple in concept, it requires the help of machines to implement it for large datasets. The clustering techniques discussed above have pros and cons, limiting their suitability to certain data sets. Analysis of the data set is not limited to the algorithm but also factors like computer hardware specifications, algorithm complexity, etc.

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