Question

Name any two clustering methods.

Hint:

K-Means is preferred for speed; Hierarchical is better for visualizing relationships among clusters.

Answer 1

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.