Unsupervised Learning: Concepts, Types and Applications

In unsupervised learning, the machine learns autonomously using unlabeled data without any supervision. Machines try to discover patterns in unlabeled data and react accordingly.

As shown below, we are based on the previous example. This time we will not tell the machine in advance whether it is a spoon or a knife. The machine will recognize the given data and group them based on characteristics such as patterns, similarities, etc.

Types of unsupervised learning

1. Clustering

Clustering is to divide objects into Methods that are similar to each other but different from objects belonging to another class.

For example, a telecommunications company reduced customer churn by offering personalized calling and data plans. They study customer behavior and use models to segment customers with similar characteristics. They then employ a variety of strategies, such as appropriate promotions and events, to minimize customer churn and maximize profits. Doing so can better meet customer needs and improve customer satisfaction, thereby increasing customer loyalty and retention. Through personalized solutions, telecommunications companies can effectively retain existing customers and attract new customers to join, thereby achieving long-term business goals.

2. Association

Association is a rule-based machine learning that is used to discover the probability of items in a collection appearing at the same time.

For example, suppose customer A goes to the supermarket to buy bread, milk, fruit and wheat. Another customer B comes to buy bread, milk, rice and butter. Now, when customer C comes, if he buys bread, he will most likely want milk as well. Therefore, based on the purchasing pattern of customer behavior, we can build a relationship and recommend customer C to buy milk. This increases sales and satisfies customer needs.

Practical application of unsupervised learning

1. Commodity analysis

It is a machine learning model based on an algorithm, that is, if you buy a certain set of commodities, you buy another The likelihood of a group of items decreases or increases.

2. Semantic clustering

Semantically similar words share similar contexts. People post queries on the website in their own way. Semantic clustering groups all these responses with the same meaning into a cluster to ensure customers find the information they are looking for quickly and easily. It plays an important role in information retrieval, good browsing experience and understanding.

3. Supply and demand optimization

Machine learning models are used to predict demand and keep up with supply. They are also used to open stores where demand is higher and optimize root causes for more efficient delivery based on past data and behavior.

4. Identify accident-prone areas

Unsupervised machine learning models can be used to identify accident-prone areas and introduce safety measures based on the intensity of these accidents.

The above is the detailed content of Unsupervised Learning: Concepts, Types and Applications. For more information, please follow other related articles on the PHP Chinese website!