In machine learning, there are two main types of learning: supervised and unsupervised. Most people are familiar with supervised machine learning but question how unsupervised learning can be useful. Both have different strengths and weaknesses; however, together, they can be used for a number of applications. Understanding unsupervised learning, its applications, and the benefits and limitations could help you get more out of your data.
What is unsupervised learning?
Unsupervised learning is a type of machine learning where the computer is given data but not told what to do with it. This is in contrast to supervised learning, where the computer is given data and also told what to do with it. Unsupervised learning can be used for tasks such as clustering, where the computer tries to group data together, or anomaly detection, where the computer tries to find data that doesn’t fit the pattern.
Unsupervised learning algorithms
There are several different types of unsupervised learning algorithms, including:
- Clustering algorithms: These algorithms are used to group similar items together. Some common clustering algorithms include k-means clustering and hierarchical clustering.
- Association rule mining algorithms: These algorithms are used to find relationships between items in a dataset. Some common association rule mining algorithms include Apriori and Eclat.
- Dimensionality reduction algorithms: These algorithms are used to reduce the number of dimensions in a dataset without losing information. Some common dimensionality reduction algorithms include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE).
Unsupervised learning applications
There are many real-world applications of unsupervised learning. Some of the most common ones include:
- Clustering - Clustering is the process of grouping data together into clusters. This can be useful for things like finding groups of customers who have similar needs or finding out whether different parts of a dataset are related. Clustering can also be used for anomaly detection.
- Anomaly detection - Anomaly detection is the process of finding data that doesn’t fit the pattern. This can be useful for tasks such as identifying errors in data or detecting fraud.
- Association rule learning - Association rule learning is the process of finding relationships between items in a dataset. This can be useful for tasks such as understanding customer behavior.
- Neural networks - Neural networks are a type of machine learning algorithm that are used for tasks such as image recognition and natural language processing. In image recognition and natural language processing, the computer is given a set of images or text and must find patterns in them. Neural networks are often trained using unsupervised learning algorithms.
- Understanding data sets - By looking at data without any labels, the computer can learn about the structure of the data and how different parts of it are related. This can be useful for things like improving search results or understanding how customers interact with a website.
Benefits of unsupervised learning
Find Patterns - One of the main benefits of unsupervised learning is that it can be used to find patterns in data that you wouldn’t have been able to find otherwise. This can be especially useful when your data is very noisy or when you don’t have any labeled training data. This can be useful for tasks such as market analysis or trend detection.
Improve supervised algorithms - Unsupervised learning can be used to improve supervised learning algorithms without using labeled data.
Reduce overfitting - Another benefit of unsupervised learning is that it can help reduce overfitting. Overfitting is when a machine learning algorithm learns the training data too well, so well that it doesn’t perform well on new data. Unsupervised learning can help reduce overfitting by providing a way to “reset” the machine learning algorithm and force it to learn from scratch.
Self-learning algorithms - Unsupervised learning can be used for self-learning algorithms. Self-learning algorithms are algorithms that can learn on their own, without any human input. This is a very important property for artificial intelligence applications.
Ability to use - Unsupervised learning is that it can be used when you don’t have any labeled training data. For example, if you wanted to build a model to predict whether or not someone will default on their loan, you could use unsupervised learning to learn about the features of people who have defaulted in the past. This would allow you to train your model even if you don’t have any information about which specific people will default on their loans.
The limitations of unsupervised learning
Like all machine learning algorithms, unsupervised learning has its limitations. Some of the main limitations of unsupervised learning include:
- Limited use - Unsupervised learning can only be used when you have data but don’t have any labels. If you have labels, then you can use supervised learning instead.
- Limited understanding - Unsupervised learning can only be used to find patterns in data, it can’t be used to understand the meaning of the data.
- Cannot solve all problems - Unsupervised learning cannot solve all problems. For example, it alone cannot be used for tasks such as image recognition or natural language processing. However, unsupervised learning algorithms can be trained to support these tasks.
- Requires a lot of data - Unsupervised learning requires a lot of data in order to find patterns. This can be a problem if you don’t have enough data or if your data is not high quality.
Despite these limitations, unsupervised learning is still a powerful tool that can be used for many different tasks.
Conclusion
In unsupervised learning, the computer is given data but not told what to do with it. This is in contrast to supervised learning, where the computer is given data and also told what to do with it. Unsupervised learning can be used for tasks such as clustering, anomaly detection, association rule learning, and understanding data sets. There are many benefits of using unsupervised machine learning, despite its limitations. If you have questions about how your business can benefit from using unsupervised machine learning, contact one of our DATA BOSSES!