Clustering Algorithms in ML

Clustering is a type of unsupervised learning in machine learning where similar data points are grouped together. Unlike classification, where data points are labeled, clustering finds hidden patterns or structures in data without prior labels. It is widely used in customer segmentation, anomaly detection, and recommendation systems.

Imagine you have a collection of objects, and you want to organize them into groups based on their similarities. Clustering algorithms help in achieving this by analyzing data and grouping similar data points together. Each group formed is called a “cluster” and the goal is to ensure that objects in the same cluster are more similar to each other than to those in other clusters.

Types of Clustering Techniques

Different types of clustering techniques are as follows:

• Partitioning Clustering
• Hierarchical Clustering

 

 

Partitioning Clustering

Partitioning clustering methods divide the dataset into a fixed number of clusters. The most common algorithm in this category is K-Means.

• Requires specifying the number of clusters beforehand.
• Efficient for large datasets.
• Works well when clusters are spherical and evenly sized.

Hierarchical Clustering

Hierarchical clustering builds a hierarchy of clusters either by merging smaller clusters into larger ones (agglomerative) or by splitting a large cluster into smaller ones (divisive). This method is represented using a dendrogram, which helps visualize the clustering process.

• Does not require the number of clusters in advance.
• Produces a tree-like structure (dendrogram) showing the merging or splitting process.
• Computationally expensive for large datasets.

Applications of Clustering

Clustering is widely used in various domains:

• Customer Segmentation: Businesses use clustering to group customers based on purchasing behavior.
• Image Segmentation: Used in medical imaging to identify different tissue types.
• Anomaly Detection: Detecting fraud in transactions or identifying network intrusions.
• Recommendation Systems: Grouping users with similar interests to provide personalized recommendations.

Clustering is an essential technique in machine learning that helps discover patterns in data without predefined labels. Choosing the right clustering algorithm depends on the nature of the data and the problem at hand. Hierarchical clustering is useful for detailed cluster analysis, while partitioning methods like K-Means are efficient for handling large datasets.