What is Clustering?
Clustering, or cluster analysis is a machine learning techniques that groups the unlabeled dataset.
It can be defined as “a way of grouping data points into different clusters, consisting of similar data points. Objects with possible similarities remain in a group that has fewer or no similarities with another group.”
Algorithms of Clustering
Pros and cons of clustering models
| Clustering Model | Pros | Cons |
|---|---|---|
| K-Means | Easy to understand. Easy to apply. Works great regardless of size. Fast, efficient, and high performance. | The number of clusters must be predetermined. |
| Hierarchical Clustering | The optimal number of clusters can be obtained from the model itself. Practical visualization through dendrogram. | Not suitable for large data sets. |