Cluster is a group of objects which have similar properties and belong to the same class.

What is Clustering?

Clustering is an unsupervised learning technique which is used to make clusters of objects i.e. it is a technique to group objects of similar kind in a group. In clustering, we first partition the set of data into groups based on the similarity and then assign the labels to those groups. Also, it helps us to find out various useful features that can help in distinguishing between different groups.

Types of Clustering

Most common categories of clustering are:-

• Partitioning Method
• Hierarchical Method
• Density-based Method
• Grid-based Method
• Model-based Method

Partitioning Method

Partitioning method classifies the group of n objects into groups based on the features and similarity of data.

The general problem would be like that we will have ‘n’ objects and we need to construct ‘k’ partitions among the data objects where each partition represents a cluster and will contain at least one object. Also, there is an additional condition that says each object can belong to only one group.

The partitioning method starts by creating an initial random partitioning. Then it iterates to improve the partitioning by moving the objects from one partition to another.

k-Means clustering follows the partitioning approach to classify the data.

Hierarchical Method

The hierarchical method performs a hierarchical decomposition of the given set of data objects. It starts by considering every data point as a separate cluster and then iteratively identifies two clusters which can be closest together and then merge these two clusters into one. We continue this until all the clusters are merged together into a single big cluster. A diagram called **Dendrogram **is used torepresent this hierarchy.

There are two approaches depending on how we create the hierarchy −

• Agglomerative Approach
• Divisive Approach

Agglomerative Approach

Agglomerative approach is a type of hierarchical method which uses bottom-up strategy. We start with each object considering as a separate cluster and keeps on merging the objects that are close to one another. It keep on doing so until all of the groups are merged into one or until the termination condition holds.

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