Artem Oppermann | Jan 06, 2023

Unsupervised learning allows machine learning algorithms to work with unlabeled data to predict outcomes. Both supervised and unsupervised models can be trained without human involvement, but due to the lack of labels in unsupervised learning, these models may produce predictions that are highly varied in terms of feasibility and require operators to check solutions for viable options.


When Is Unsupervised Learning Used?

We often use unsupervised learning to perform more complex processing tasks, such as clustering large quantities of data.

Unsupervised learning is often used to perform more complex processing tasks, such as clustering large quantities of data. Unlabeled data is more plentiful than labeled data and requires no human intervention before training the unsupervised models, which adds to unsupervised learning’s usefulness.

Unsupervised machine learning is particularly useful for uncovering unknown patterns of data that can be further analyzed for feasibility and correctness. Additionally, we use unsupervised learning to discover features within submitted data that can be categorized in unexpected ways. This process requires further analysis to ensure these categories make sense.

Unsupervised Learning | Video: CrashCourse


What Are Different Types of Unsupervised Learning?

The most common use of unsupervised learning is in clustering wherein different algorithms create multiple functionalities.

Unsupervised learning models are incredibly useful when it comes to organizing elements of large data sets into clusters for further analysis. Several different clustering algorithms exist to allow data to be organized as necessary.

Unsupervised Learning Clustering Algorithm Examples

  • Exclusive algorithms, also known as partitioning, allow data to be grouped so that a data point can belong to one cluster only.
  • Agglomerative algorithms make every data point a cluster and create iterative unions between the two nearest clusters to reduce the total number of clusters.
  • Overlapping algorithms use fuzzy sets to cluster data points in two or more clusters with separate degrees of membership.
  • Probabilistic algorithms use probability to distribute data into different clusters.

Some examples of clustering algorithms include hierarchical clustering, K-means clustering, KNN clustering, principal component analysis, singular value decomposition and independent component analysis.

Aside from clustering, unsupervised learning can also be used for association, which allows models to create relationships between elements within databases and uncover unseen relationships between variables.

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What Is the Difference Between Supervised and Unsupervised Learning?

Supervised learning relies on using labeled data sets to operate. Unsupervised learning does not.

Supervised learning is less versatile than unsupervised learning in that it requires the inputs and outputs of a data set to be labeled to provide a correct example for machine learning models to weigh predictions against. In other words, supervised learning requires human intervention to label data before the model is trained. 

Unsupervised learning models take longer to train and may produce less accurate outcomes than supervised models but can utilize unlabeled data to provide results. Ultimately, unsupervised learning is best used for analyzing and clustering large quantities of output data for further analysis, using methods such as principal component analysis, association and dimensionality reduction.

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