What is Unsupervised Learning?

Unsupervised learning is a type of machine learning algorithm that looks for patterns in a dataset without pre-existing labels.
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As the application of artificial intelligence continues to grow, it’s important to know the different types of AI and machine learning available. Today, we’ll be discussing Unsupervised learning, this type of AI is what Unsupervised uses to power our Data Capitalization Management platform. Keep reading to learn more about how unsupervised learning empowers humans to make better decisions.

What is Unsupervised Learning?

Unsupervised learning is a type of machine learning algorithm that looks for patterns in a dataset without pre-existing labels. As the name suggests, this type of machine learning is unsupervised and requires little human supervision and prep work. Because unsupervised learning does not rely on labels to identify patterns, the insights tend to be less biased than other forms of AI.

How is Unsupervised Learning Used?

Unsupervised learning models are used in the following ways:

  1. Clustering: This is the process of finding similarities among unlabeled data and grouping them together.
  2. Association: This unsupervised learning method finds relationships between the data in a given dataset.
  3. Dimensionality Reduction: This machine learning technique is used when the number of features in a dataset is too high. This technique reduces the number of inputs into a more manageable size all while preserving the data integrity.

What is the Difference Between Supervised and Unsupervised Machine Learning?

The difference between supervised and unsupervised learning is pretty clear: supervised learning uses labeled datasets, and unsupervised learning does not. Supervised learning requires more human preparation at the beginning of the process due to the need for proper labeling. The labeling is used as a guide to determine if its pattern recognition is accurate or not.

Unsupervised learning models work independently on unlabeled datasets with little human supervision.  Because unsupervised learning does not require a hypothesis to identify patterns, it removes hypothesis bias. It does, however, require humans to validate output variables in order to confirm what is important.

What are the Benefits of Unsupervised Learning?

Using unsupervised machine learning algorithms on your data has many benefits. Here are some of the most common reasons people turn to unsupervised learning:

  • It can handle large amounts of unlabeled and unstructured data.
  • It makes it easier and faster to analyze complex data.
  • It is able to identify previously undetected patterns.
  • It learns about your data so it can teach you what you don’t know.

Unsupervised learning helps humans make better decisions which is why businesses turn to Unsupervised, the largest vendor of this type of AI, to get super-specific insights on their most complex datasets. The Unsupervised platform acts as a teacher, gauging how people learn and understand data in order to provide better insights.

Want to see the Unsupervised platform in action? Watch our 5-minute demo.

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