Types of Clustering
Broadly speaking, there are 2 types of clustering that can be performed to group similar data points:
- Hard Clustering: In this type of clustering, each data point belongs to a cluster completely or not. For example, Let’s say there are 4 data point and we have to cluster them into 2 clusters. So each data point will either belong to cluster 1 or cluster 2.
Data Points | Clusters |
---|---|
A | C1 |
B | C2 |
C | C2 |
D | C1 |
- Soft Clustering: In this type of clustering, instead of assigning each data point into a separate cluster, a probability or likelihood of that point being that cluster is evaluated. For example, Let’s say there are 4 data point and we have to cluster them into 2 clusters. So we will be evaluating a probability of a data point belonging to both clusters. This probability is calculated for all data points.
Data Points | Probability of C1 | Probability of C2 |
A | 0.91 | 0.09 |
B | 0.3 | 0.7 |
C | 0.17 | 0.83 |
D | 1 | 0 |
Clustering in Machine Learning
In real world, not every data we work upon has a target variable. This kind of data cannot be analyzed using supervised learning algorithms. We need the help of unsupervised algorithms. One of the most popular type of analysis under unsupervised learning is Cluster analysis. When the goal is to group similar data points in a dataset, then we use cluster analysis. In practical situations, we can use cluster analysis for customer segmentation for targeted advertisements, or in medical imaging to find unknown or new infected areas and many more use cases that we will discuss further in this article.
Table of Content
- What is Clustering ?
- Types of Clustering
- Uses of Clustering
- Types of Clustering Algorithms
- Applications of Clustering in different fields:
- Frequently Asked Questions (FAQs) on Clustering