Introduction To 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, where the data is labeled and the model learns to predict the output from the input data, unsupervised learning models find hidden patterns or intrinsic structures in the input data.

Types of Unsupervised Learning

Unsupervised learning can be broadly categorized into clustering and association problems:

Clustering: Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups.
Association: Association is a rule-based method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.

Clustering Example: K-Means Clustering

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. The objective of K-means is to group similar data points together and discover underlying patterns. Here is an example of how K-means clustering works:

Consider we have the following dataset:

[[2, 3], [3, 4], [10, 12], [11, 13], [20, 30]]

We want to cluster this data into 2 clusters. Using the K-means algorithm, the dataset will be divided into two clusters:

Cluster 1: [[2, 3], [3, 4]]
Cluster 2: [[10, 12], [11, 13], [20, 30]]

Association Example: Market Basket Analysis

Market Basket Analysis is a popular technique used by large retailers to uncover the associations between items. It works by looking for combinations of items that frequently co-occur in transactions. Here is an example rule derived from market basket analysis:

If a customer buys bread, they are likely to also buy butter.

This can be represented as an association rule:

bread => butter

Such rules can help retailers to understand the purchase behavior of customers and can be used for cross-selling, up-selling, and improving customer satisfaction.

Applications of Unsupervised Learning

Unsupervised learning has a wide range of applications in different fields:

Market Basket Analysis: Identifying products frequently bought together.
Customer Segmentation: Grouping customers based on their purchasing behavior.
Anomaly Detection: Identifying unusual data points or outliers.
Dimensionality Reduction: Reducing the number of random variables under consideration.
Image Compression: Reducing the size of image files.

Conclusion