Clustering is an unsupervised technique in the field of machine learning algorithms. The task of this technique is to convert complex dataset into different groups based on their features and characteristics.
It plays a significant role in artificial intelligence. In various domains it offers insights in underlying patterns and trends of dataset And makes it easy to find the needed information and also helps in eliminating outliers, extraneous characteristics. It also makes it easy for us to understand by visualising the dataset.
Clustering has many types of algorithms but K-means and density-based clustering plays a significant role in identifying and structuring large datasets And make it easy for us to understand the Information and make predictions
If you want to know the underlying patterns and trends of the dataset, clustering Is here to help you out.
How Clustering works
As we know what clustering actually is, now we will dive further into it and understand how it works.
There are four main steps involved in this process.
| Step 1 | Step 2 | Step 3 | Step 4 |
| Preparation of data | Similarities | Algorithms | Clusters |
After collecting the data it measures the similar points, features and characteristics. It varies according to the given data. Clustering has different algorithm methods with which it categorises unsupervised data.
There are two subgroups of Clustering
Imagine you are the owner of a store and to increase the growth of your business you want to know the demands of your customers. But is it possible for you to complete all demands of all of your customers? Obviously not, what you will do is make a few groups of the high demands of your customers and then use business strategies to fulfil those needs.
Hard Clustering:
When a specific characteristic or point of the given data only belongs to one group of clusters is called Hard Clustering for example each customer according to their specific need will have to be assigned in the specifically organised group by the owner for this k – means centroid algorithm is used.
Soft clustering:
But in this group any point of the given dataset can belong to more than one group, for example customers can be assigned into any of the organised groups this method is called Gaussian distribution model.
Type of methods used in Clustering
To define the Similarities in the dataset many different algorithms methods are used. Here are some of the most popular algorithms in the USA and UK.
| Centroid Clustering Centroid is also known as the center of a cluster. It segregates the data points based on their similarities. The most commonly used method for this is the K-means algorithm. It splits the data according to the distance from the centroid. The major drawback for the K-means method is the requirement of prior knowledge of data. |
| Density-Based Clustering It is also a very popular algorithm for clustering. It separates the data according to its density levels. It makes a group of high density data and another group of low density data. One of the best things about density-based clustering is that it does not need any prior knowledge before performing any action. It only depends upon the density. It can handle different sizes of data and any outliers and noises very effectively. |
| Hierarchical Clustering It’s a powerful algorithm technique in which the same points of data are grouped together according to their features like distance or similarities. As its name shows the value of its work. It gives a deeper understanding of data. Hierarchical clusters can be formed by two Methods. Agglomerative: It performs clustering by using bottom up strategy. In this all data is divided into different clusters and then all those clusters are grouped together to make one big cluster. Observation runs in each group. Observation is a very important step in this as it allows deeper understanding of data and identify patterns. Divisive: It performs clustering by using top down strategy. All data is grouped together to make one big cluster and then this big one is further divided into specific tree-like structures. This structure facilitates the data to split to the levels of clusters. |
Conclusions
Suppose you are working with a very complex dataset as a Netflix organiser. You want to make it easy for your audience to find similar data so you categorised their similar patterns and numeric features. This categorisation is called clustering, its goal is to collect similar points of data.
Popular platforms like Amazon, Google, prime video etc are the real world examples of clustering.
FAQs
Isn’t clustering and classification the same thing?
Basically no, classification uses labeled data and makes predictions. However clustering uses unlabelled data and identifies patterns and structures of data.
Which one is the quickest clustering method?
K-means is the most popular and quickest method of clustering.
What is the main point of clustering?
Large data is divided into different groups with the help of algorithms. You can observe the pattern and structures.
