Machine Learning in general is generally divided into two types, Supervised and Unsupervised Learning. Unsupervised Learning is also further divided into two main types. These are:

• Clustering: A clustering problem is where there is a need to bring out the inherent groupings in data. Eg. – Grouping customers by their purchasing behavior.
• Association:  An association rule learning problem is implemented when we want to discover those rules that describe large portions of our data. Eg. – The recommended content on most of the Online Shopping Websites, Social Networking Sites, etc. of the type “People that buy X also tend to buy Y.”

In this article we will learn more about clustering and how it is used!

Clustering, which is one of the forms of Unsupervised Learning, is where there is only the input data (X) and no corresponding variables like the dependent variable (y) or the variable which needs to be predicted. The goal of unsupervised learning is to model the underlying structure or distribution in the data to work and develop more facts about the data. This learning is called unsupervised learning because unlike supervised learning, there are no correct answers, and there is no teacher like the training set data that is used in the supervised learning in regression and classification. Algorithms are left to their devices to discover and present the impressive structure in the data.

Clustering Methods

 Clustering methods are broadly classified into the following categories −

• Partitioning Method – Partitioning ‘n’ objects into ‘k’ partitions of data.
• Hierarchical Method – It creates a hierarchical decomposition of the given set of data objects.
• Density-based Method – The basic idea of this approach is to continue growing the given cluster for as long as the density in a particular neighborhood exceeds some fixed threshold.
• Grid-Based Method – Here, the objects together form a grid.
• Model-Based Method – Here, a model is hypothesized for each cluster to find the best fit of data for a given model.
• Constraint-based Method – In this method, the clustering is performed by the incorporation of the user or application-oriented constraints.

It would not be inappropriate to say that life would be too difficult for us, if not for the assistance of clustering in our daily walks of life. Clustering finds its use in the industry in many ways. Some of them are:

• It can also help the marketing managers to discover distinct groups and sub-groups in their customers based on their similarities, like the Age group, have a car or not, average expense, etc. which can undoubtedly help in using tactics for a better sale.
• Clustering analysis is broadly used and finds its applications in market research, unique pattern recognition, image processing, and data analysis.
• Identification of particular areas of similar land use in the Earth Observation Database, which also finds implementation in the identification of groups of houses in a city based on the house type, value, and geographic location.
• In the Biological field, it can be used to derive animal and plant taxonomies, categorize their genes with similar and dissimilar functionalities and gain insight into structures inherent to populations.
• The publicly available Taxi service provided by Uber, Ola, etc. process a large amount of valuable data using Clustering around traffic, transit-time, peak pickup localities, and more.
• Classifying the documents on the web for Information discovery like a search Engine.
• Outlier detection applications. Eg. : Detection of the Credit card fraud.
• Clustering is also helpful in identifying Crime localities that require the special attention of the police.
• The broadest and most extensive usage of Clustering is implemented in Data-mining. It is a technique by which different data elements are classified and put into related groups.
• Call Record Detail Analysis (CDR) is the information captured by the worldwide telecom companies during the call, SMS, and the data usage activity of a customer.

Concluding, Unsupervised Learning portrays an extraordinary role in visualizing facts and figures, that can’t be seen and observed by human eyes. This processed information is not just useful for a company, but it has extensive application on a broad industry scale.