This article explains unsupervised learning and how it works from an artificial intelligence (AI) perspective.
Unsupervised learning is a type of machine learning used to identify patterns in unlabeled data sets.
Unsupervised learning algorithms find patterns in large, unsorted data sets without the need for human guidance or supervision.
They can group large amounts of data points to derive insights faster and more efficiently than any human data scientist.
Once the algorithm is fed unstructured data, the machine learning process becomes fully automated. Ideally, these algorithms will improve real-time classification as new relationships are established between data points (or inputs).
For example, an unsupervised learning algorithm given images of different shapes might start ranking each shape based on its size and color. The algorithm can then become more specific by classifying shapes based on their number of sides.
Unsupervised learning is helpful in many areas of artificial intelligence, including:
- Cybersecurity : Detect and block cyberattacks before they happen.
- Computer Vision : Recognizing images, videos, and real-life objects.
- Fraud detection : Flag suspicious documents or financial transactions.
- Healthcare : Diagnosing diseases and developing medicines.
- Marketing : Targeting ads to customers based on their preferences.
- News aggregation : Sort news stories by topic, region and interest.
- Quality Assurance : Identify anomalies and outliers in equipment and products.
Unsupervised learning is often used along with supervised learning, which relies on human-labeled training data. In supervised learning, humans determine the ranking criteria and output of the algorithm.
This gives people more control over the type of information they want to extract from large data sets. However, supervised learning requires more human time and expertise.
Unsupervised methods are appropriate when you have large amounts of unorganized data. With unsupervised learning, no one needs to analyze or label anything. Therefore, unsupervised learning is less expensive than supervised learning because it requires less human effort.
Semi-supervised learning algorithms combine these two methods by comparing labeled and unlabeled data in an initial training set.
The results of unsupervised learning can be unpredictable and sometimes even unhelpful.
If an algorithm is too specific, it may create too many categories, making it difficult for humans to draw meaningful insights from the output. On the other hand, if the algorithm is too general, there will be too few categories.
Since all data is unlabeled, accuracy can be difficult to verify, and it can be difficult to determine how accurately unsupervised learning algorithms make decisions.
Unsupervised learning requires more computing power and time, but it is still cheaper than supervised learning because no humans are required.
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 used by unsupervised learning algorithms include:
- Exclusive clustering : Each data point can belong to only one cluster or group (e.g., K-means clustering).
- Overlapping clusters : A data point can be part of multiple clusters with different levels of association.
- Agglomerative clustering : Data points are divided into groups and merged into a cluster.
- Probabilistic clustering : Data points are grouped according to probability distributions.
- Apriori Algorithm : Use frequently occurring data points to make predictions and recommendations.
- Dimensionality reduction : Eliminate redundant data and reduce the data set to a more manageable size.
- Autoencoding : Neural networks are used to compress and represent the same data in different ways.
K-clustering, often called K-means clustering, refers to organizing data based on similarities and differences between clusters. K is used to represent the number of clusters.
This is a way to gather information about information. So, once the data is collected, it is classified into similar groups and finally organized into sections and subsections. Some of us who are more fiscally responsible have done this by focusing our spending on housing, housing and transportation. But when you cluster further, you find that transportation can be further clustered into public transportation, our cars, etc. Then, under the car, you might also include maintenance, fuel, cleaning, etc. Computers do this calculation on much larger scales and on many different data sets, usually not about how many lattes were consumed before 10:30 AM.
