Types of Unsupervised Learning
Types of Unsupervised Learning
Machine Learning is a branch of Artificial Intelligence that enables computers to learn from data and make decisions without being explicitly programmed. There are two main types of learning: Supervised and Unsupervised. In this article, we will focus on Unsupervised Learning.
What is Unsupervised Learning?
Unsupervised Learning is a type of machine learning where the algorithm is trained on unlabeled data. Unlike supervised learning, there are no predefined outputs or labels. The goal is to identify patterns, structures, and relationships within the data. It is used when we do not know the exact output we are looking for. It is often applied in fields such as customer segmentation, anomaly detection, data compression, etc.
Types of Unsupervised Learning
The main types of unsupervised learning are as follows:
- Clustering
- Association
- Dimensionality Reduction
Clustering
Clustering is the process of grouping similar data points together based on their characteristics. It is useful for segmenting large datasets into meaningful groups. This method divides the items into clusters that are similar between them and are dissimilar to the items belonging to other clusters.
Example
An e-commerce company can use clustering to group customers based on their purchasing behavior. This helps in targeting specific groups with personalized marketing strategies.
Association
Association is a rule-based ML to discover the probability of the co-occurrence of items in a collection. It discovers relationships between variables in large datasets.
For example, this technique can be used to find out which products were purchased together. It is used in market basket analysis to find items that are frequently bought together.
Example
A supermarket might find that customers who buy milk are likely to buy bread as well. This insight can be used for better product placements, recommendations, and promotions.
Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of features in a dataset while preserving important information. It helps in improving the performance of machine learning models by removing redundant data.
Example
In image processing, high-dimensional images can be compressed into lower dimensions without losing key features, making analysis faster and more efficient.
Unsupervised learning plays a crucial role in data exploration and pattern recognition. It helps in understanding complex datasets and extracting useful insights without the need for labeled data.
