Clustering

There are various clustering methods used to group data based on similarity measures.
Two popular clustering methods are:
1) K-Means Clustering:
- It is one of the most widely used partitioning methods.
- The algorithm divides the data into a pre-defined number of clusters (k).
- It assigns each data point to the nearest cluster center and updates the centers iteratively to minimize within-cluster variance.
- K-Means is simple, efficient, and works well for large datasets.
2) Hierarchical Clustering:
- This method builds a hierarchy of clusters either by merging smaller clusters into bigger ones (agglomerative) or splitting larger clusters into smaller ones (divisive).
- The result is often visualized using a dendrogram, which shows how clusters are related and merged at each step.
- Hierarchical clustering does not require specifying the number of clusters in advance.
- It is useful for exploratory data analysis and understanding the nested structure of data.
Both these methods help data analysts and businesses discover meaningful patterns and make informed decisions.

Clustering is an unsupervised machine learning technique used to automatically group similar data points into clusters based on certain characteristics or patterns.
Unlike supervised learning, clustering does not require labelled data.
The main goal is to maximize similarity within clusters and minimize similarity between clusters.
It is a key tool in exploratory data analysis, helping to uncover hidden structures and relationships in datasets.
Clustering is widely used in data mining, market segmentation, image recognition, and anomaly detection.
Real Life Example:
An e-commerce company uses clustering to segment its customers into different groups based on purchase behavior.
Customers with similar buying patterns are grouped together.
This allows the company to design targeted marketing strategies, recommend relevant products, and improve customer satisfaction.
For example, frequent buyers of sports gear may receive special offers on new sports collections.
Clustering thus enables businesses to understand their audience better and make data-driven decisions.