What Is Unsupervised Machine Learning?

This article explains unsupervised learning and how it works from an artificial intelligence (AI) perspective.

What is Unsupervised Learning?

Unsupervised learning is a type of machine learning used to identify patterns in sets of unlabeled data.

How Does Unsupervised Machine Learning Work?

Unsupervised learning algorithms find patterns in large unsorted data sets without human guidance or supervision.

They can group data points within vast sets, allowing them to draw insights faster and more efficiently than any human data scientist.

The machine learning process is completely automated once the algorithm is fed the unstructured data. Ideally, these algorithms will improve at real-time categorization as they establish new relationships between data points (or inputs).

For instance, an unsupervised learning algorithm given images of different shapes might start sorting each shape according to its size and color. Then, the algorithm may get more specific by classifying shapes based on their number of sides.

Applications of Unsupervised Learning

Unsupervised learning has been helpful in many areas of AI, including:

• Cybersecurity: Detecting and intercepting cyber attacks before they happen.
• Computer vision: Recognizing objects in images, videos, and real life.
• Fraud detection: Flagging suspicious documents or financial transactions.
  
• Healthcare: Diagnosing illnesses and developing medications.
• Marketing: Targeting ads to customers based on their preferences.
• News aggregation: Sorting news stories based on topic, region, and interests.
• Quality assurance: Identifying abnormalities and outliers in equipment and products.
  

Supervised vs. Unsupervised Learning

Unsupervised learning is often used with supervised learning, which relies on training data labeled by a human. In supervised learning, a human decides the sorting criteria and outputs of the algorithm.

This gives people more control over the types of information they want to extract from large data sets. However, supervised learning requires more human time and expertise.

An unsupervised approach is appropriate when you have a large quantity of unorganized data. With unsupervised learning, no one needs to analyze or label anything. Thus, unsupervised learning costs less than supervised learning since it requires less human labor.

Semi-supervised learning algorithms combine both approaches by comparing labeled and unlabeled data in the initial training set.

Limitations of Unsupervised Learning

The results of unsupervised learning can be unpredictable and sometimes even unhelpful.

If the algorithm gets too specific, it might create too many categories, making it difficult for humans to draw meaningful insights from the outputs. On the flip side, if the algorithm is too general, there will be too few categories.

Accuracy can be hard to verify since all the data is unlabeled, and it can be difficult to determine how exactly unsupervised learning algorithms make their decisions.

Unsupervised learning takes more computing power and time, but it's still cheaper than supervised learning because no human involvement is needed.

Types of Unsupervised Learning Algorithms

Many unsupervised learning algorithms are based on cluster analysis, or clustering, which involves grouping objects based on their similarities and differences. Some of the methods unsupervised learning algorithms use include:

• Exclusive clustering: Each data point can only belong to one cluster or group (for example, K-means clustering).
• Overlapping clustering: Data points can be part of multiple clusters with different levels of association.
• Agglomerative clustering: Data points are separated into groups and merged into one single cluster.
• Probabilistic clustering: Data points are grouped based on probability distribution.
• Apriori algorithms: Frequently occurring data points are used to make predictions and recommendations.
• Dimensionality reduction: Excess data is eliminated to reduce the dataset to a more manageable size.
• Autoencoding: A neural network is used to compress and represent the same data differently.