What is Clustering in Machine Learning?

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What is Clustering in Machine Learning

Clustering or cluster analysis in Machine Learning is a technique that involves the grouping of unlabeled datasets. A clustering algorithm is used to categorise each dataset into a specific group. It is an unsupervised learning method and is used widely for statistical data analysis across multiple fields, including AI and Machine Learning. Clustering works by finding similar patterns in the unlabeled data points such as size, shape, behaviour, etc., and classifies them as per the presence and absence of such similar patterns. Once the clustering technique is applied, each group is given a cluster-ID to be used by the AI and Machine Learning system to process large and complex datasets quickly.

Why Clustering?

The clustering technique is used commonly in various tasks, and some of the most common uses of this technique are:

  • Market Segmentation
  • Statistical Data Analysis 
  • Social Network Analysis 
  • Image Segmentation
  • Anomaly Detection

Apart from these routine applications, it is also used by companies like Amazon and Netflix to recommend products and movies to users based on their behaviour and browsing patterns. The use of clustering in the last few years has increased due to the widespread use of AI and Machine Learning systems by companies to target and retain their customer base.

Types of Clustering Methods

The clustering methods are broadly categorised into hard and soft clustering. In hard clustering, data points belong to one specific group, and in soft clustering, data points can be from different groups. As the best Machine Learning course provider, we have rounded up the important methods used in Machine Learning:

  1. Partitioning Clustering 
  2. Density-based Clustering 
  3. Distribution Model-based Clustering 
  4. Hierarchical Clustering 
  5. Fuzzy Clustering
Applications of Clustering

When you learn clustering techniques through a Machine Learning course, you also need to understand its applications in the real world.

  • Identification of Cancer Cells: The clustering algorithms are used in medicine to identify cancerous cells by dividing the cancerous and non-cancerous data points into different groups. 
  • Customer Segmentation: The technique is used in market research by companies to identify and segment the customers based on their behaviour, choice, and preference. 
  • In Search Engines: Search platforms such as Google and Bing also work on clustering techniques. Search engines reflect results depending upon the closest object to the search query. 
  • Biology: It is used in the field of biology to classify different plant and animal species through the image recognition technique.
Conclusion

Clustering techniques and algorithms are fundamental components that every data scientist working with AI and Machine Learning systems should know. It helps in visualising, identifying, and grouping datasets that make it easier to process them.

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