Clustering is an essential process for different organizations as it assists with numerous activities and tasks. Suppose you are part of a company’s research analysis team. You need to understand how potential customers react to the product and services you provide. But this requires valuable data that helps in understanding customer behavior. This data will help you deliver a better experience and achieve business goals. That is where the **clustering** concept becomes essential.

**What is Clustering in Machine Learning**

**Clustering** helps you organize data in different groups, depending on the features. You determine these features according to the group’s data points. Suppose you want to categorize all the types of cats in different groups, depending on their species. In this case, you will divide different types into various segments such as snowshoe, Persian, Japanese bobtail, and Siamese. This way, you can clearly analyze the types of cats under different segmentations.

This condition also applies to various machine learning problems. You can divide problems into unique categories, depending on similarities. You will provide data to the machine learning algorithm, making it an unsupervised learning option. It’s important to remember the necessity of feeding accurate data as the **clustering algorithms** group the new data depending on the requirements you provide.

**What are Clustering Algorithms?**

**Clustering** task is an unsupervised machine learning technique. Data scientists also refer to this technique as cluster analysis since it involves a similar method and working mechanism. When using **clustering algorithms** for the first time, you need to provide large quantities of data as input. This data will not include any labels. This will boost the algorithm and create different groups.

These groups will be clusters of data, aligned according to similarities. The cluster includes all the data points that share a resemblance in their features or properties. They relate to each other in one way or another. You can use **clustering** to discover patterns, engineering, and shapes. When generating insight into the data, use **clustering** as the initial process.

**Categories of Clusters**

There are two major categories of **clustering**. These are:

**Hard Clustering**

In hard **clustering**, data falls under a single cluster. That means it cannot share a group with any other category, except for one. This will depend on the priorities of the features.

**Soft Clustering**

On the other hand, it is possible that the data is subject to soft **clustering**. This means it falls under one or multiple clusters. In easy words, data can share two or more positions and fall under different groups.

**Top Clustering Algorithms**

**K Means Clustering Algorithm**

**K means clustering** is a common algorithm among data scientists. This is a type of centroid-based algorithm with simple and straightforward properties. Moreover, this is an unsupervised learning algorithm. With this algorithm, you can minimize the data point’s variance in the cluster. Many people who begin unsupervised machine learning start with **K means clustering **algorithms first**.**

You will find the best results with these **clustering algorithms**, containing small data sets. That’s because this algorithm repeats all data points. It indicates that if you have a huge quantity of data, you will need more time to cluster it all.

**Density-Based Clustering**

In this method, the **clustering algorithms** will require data density to create clusters representing the data space. When space or region grows dense, that region becomes a cluster. You will refer to the region with less density or with minimum data as outliers or noise. You will find the arbitrary shape of the data due to the method of this cluster.

**Hierarchical Clustering**

Hierarchical **clustering** groups are the clusters depending on the distance from one data to another. These clusters have various types:

**Agglomerative**

In this **clustering** method, one data point acting as a cluster will attract other similar data points becoming clusters.

**Divisive**

On the other hand, the divisive method will consider all the data points as one cluster and then separate each data point creating new clusters. This method is opposite to Agglomerative, and it works by linking the existing cluster, creating a distance matrix, and joining them together. You can represent the data point clusters with the help of a denogram.

**Fuzzy Clustering**

In this method, the alignment of the data points is not decisive. In Fuzzy **clustering**, a data point can link with more than one cluster. The outcome of the cluster is the probability of the data point **clustering **under a group. The working mechanism of the **clustering** method is similar to **K means clustering**. However, the parameters that involve computation are different.

**When will you need the Clustering Technique?**

You will use the **clustering **methods when you have different sets of unlabeled data. Initially, you will use an unsupervised learning algorithm. You can choose from numerous unsupervised techniques. Some of these techniques are reinforcement learning, neural networks, and **clustering**. You need to choose **clustering algorithms** depending on the data you need to cluster.

While trying anomaly detection, you can use **clustering **and identify the outliers of the data. You can cluster not only the data in different groups but also measure the boundaries. If you are unable to decide which **clustering algorithms** will work, start by using **K means clustering** and discover new patterns.

**Conclusion**

**Clustering algorithms** help you learn new things by using old data. You can find solutions to numerous problems by **clustering** the data in different ways. This way, you find new solutions to existing problems.

The best part about **clustering** the data in unsupervised learning is that it derives outcomes in supervised learning problems. You can use the **clustering** technique to solve any unsupervised machine learning problems. You can choose different clusters as new features and utilize them for a new data set. The result will be surprising if you continue working on enhancing accuracy.